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gather_ranges_to_dense_op.h
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gather_ranges_to_dense_op.h
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#ifndef CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_
#define CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_
#include <math.h>
#include "caffe2/core/common_omp.h"
#include "caffe2/core/context.h"
#include "caffe2/core/export_caffe2_op_to_c10.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/types.h"
#include "caffe2/utils/math.h"
#include "caffe2/utils/proto_utils.h"
#include <cstring>
#include <map>
#include <utility>
C10_DECLARE_EXPORT_CAFFE2_OP_TO_C10(GatherRangesToDense);
namespace caffe2 {
template <class Context>
class GatherRangesToDenseOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
template <class... Args>
explicit GatherRangesToDenseOp(Args&&... args)
: Operator<Context>(std::forward<Args>(args)...),
lengths_(this->template GetRepeatedArgument<int>("lengths")),
minObservation_(this->template GetSingleArgument<int64_t>(
"min_observation",
10000)),
maxMismatchedRatio_(this->template GetSingleArgument<float>(
"max_mismatched_ratio",
0.01)),
maxEmptyRatio_(
this->template GetSingleArgument<float>("max_empty_ratio", 1.0)) {
CAFFE_ENFORCE_GT(lengths_.size(), 0, "There has to be at least one length");
for (auto length : lengths_) {
CAFFE_ENFORCE_GT(length, 0, "Each length should be positive");
}
CAFFE_ENFORCE_GT(
minObservation_, 0, "The number of observations is at least 1");
// Initialize the empty and mismatch counter.
for (int i = 0; i < OutputSize(); ++i) {
emptyRanges_.push_back(0);
mismatchedRanges_.push_back(0);
}
}
~GatherRangesToDenseOp() noexcept override {
if (totalRanges_ > minObservation_) {
string debugString;
if (this->has_debug_def()) {
debugString =
"Info from operator: " + ProtoDebugString(this->debug_def());
} else {
debugString = "Info from operator: no op def";
}
LOG(INFO) << "In GatherRangesToDenseOp:\n"
<< " Lifetime empty ranges for each feature is "
<< emptyRanges_ << ".\n"
<< " Lifetime mismatched ranges for each feature is "
<< mismatchedRanges_ << ".\n"
<< " With a total of " << totalRanges_ << " examples.\n"
<< debugString;
}
}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, this->template Input<Tensor>(RANGES, CPU));
}
template <typename Index>
bool DoRunWithType() {
auto& data = Input(DATA);
auto& ranges = Input(RANGES);
CAFFE_ENFORCE_EQ(data.dim(), 1, "Data has to be 1-D");
CAFFE_ENFORCE_EQ(ranges.dim(), 3, "Ranges has to be 3-D");
if (InputSize() == 3) {
auto& key = Input(KEY);
CAFFE_ENFORCE_EQ(key.dim(), 1, "Key has to be 1-D");
CAFFE_ENFORCE(
key.dtype().template Match<int64_t>(), "Key has to be type int64_t");
}
CAFFE_ENFORCE_EQ(
ranges.size(1),
lengths_.size(),
"Nummber of ranges should match number of lengths");
CAFFE_ENFORCE_EQ(
ranges.size(1),
OutputSize(),
"Nummber of ranges should match number of outputs");
CAFFE_ENFORCE_EQ(
ranges.size(2), 2, "Ranges last dimension should be of size 2");
auto* rawData = static_cast<const char*>(data.raw_data());
auto* rangesData = ranges.template data<Index>();
int rangesDataOffset = 0;
auto itemsize = data.dtype().itemsize();
auto batchSize = ranges.size(0);
vector<int64_t> outputDims{batchSize, 0};
vector<char*> outputRawData;
for (int i = 0; i < OutputSize(); ++i) {
auto* output = Output(i);
outputDims[1] = lengths_[i];
output->Resize(outputDims);
char* ptr = static_cast<char*>(output->raw_mutable_data(data.dtype()));
memset(ptr, 0, output->nbytes());
outputRawData.push_back(ptr);
}
for (int i = 0; i < batchSize; ++i) {
for (int j = 0; j < OutputSize(); ++j) {
auto rangeStart = rangesData[rangesDataOffset++];
auto rangeLength = rangesData[rangesDataOffset++];
if (rangeLength == 0) {
// empty range, will be filled with zeros
emptyRanges_[j]++;
continue;
}
if (rangeLength != lengths_[j]) {
// Range lengths missmatch for output #, will be filled with zeros
// Note, empty ranges are not counted as mismatched because empty
// are more common and more tolerable.
mismatchedRanges_[j]++;
continue;
}
if (InputSize() == 2) {
context_.CopyItemsSameDevice(
data.dtype(),
rangeLength,
rawData + rangeStart * itemsize,
outputRawData[j] + i * itemsize * lengths_[j]);
} else {
auto& key = Input(KEY);
auto* key_data = key.template data<int64_t>();
vector<std::pair<int64_t, const char*>> buffer;
for (int b_i = 0; b_i < rangeLength; ++b_i) {
int64_t one_key_item = key_data[rangeStart + b_i];
auto* one_data_item = rawData + (rangeStart + b_i) * itemsize;
buffer.emplace_back(one_key_item, one_data_item);
}
std::sort(
buffer.begin(),
buffer.end(),
[](const std::pair<int64_t, const char*>& left,
const std::pair<int64_t, const char*>& right) {
return left.first < right.first;
});
for (int b_i = 0; b_i < rangeLength; ++b_i) {
// Since this CPU only, directly copy to the destination.
std::memcpy(
outputRawData[j] + (i * lengths_[j] + b_i) * itemsize,
buffer[b_i].second,
itemsize);
}
}
}
}
CAFFE_ENFORCE_EQ(rangesDataOffset, ranges.numel());
// Check whether the empty and mismatch ratio exceeded the threshold.
totalRanges_ += batchSize;
for (int j = 0; j < OutputSize(); ++j) {
// Only check when the ratio is not set to allow all mismatches.
if (maxMismatchedRatio_ < 1.0) {
CAFFE_ENFORCE_GE(
std::max(totalRanges_, minObservation_) * maxMismatchedRatio_,
mismatchedRanges_[j],
"Ratio of range length mismatch for feature at index ",
j,
" is ",
(static_cast<double>(mismatchedRanges_[j]) /
static_cast<double>(totalRanges_)),
" (",
mismatchedRanges_[j],
"/",
totalRanges_,
") which exceeds ",
maxMismatchedRatio_);
}
// Only check when the ratio is not set to allow all examples to be empty.
if (maxEmptyRatio_ < 1.0) {
CAFFE_ENFORCE_GE(
std::max(totalRanges_, minObservation_) * maxEmptyRatio_,
emptyRanges_[j],
"Ratio of empty ranges for feature at index ",
j,
" is ",
(static_cast<double>(emptyRanges_[j]) /
static_cast<double>(totalRanges_)),
" (",
emptyRanges_[j],
"/",
totalRanges_,
") which exceeds ",
maxEmptyRatio_);
}
}
return true;
}
INPUT_TAGS(DATA, RANGES, KEY);
private:
vector<int> lengths_;
int64_t totalRanges_ = 0;
vector<int64_t> emptyRanges_;
vector<int64_t> mismatchedRanges_;
// To avoid false alarm due to insufficient sample (e.g., first batch being
// mismatched and causing 100% to be mismatched), use a threshold to ensure
// enough samples are gathered before decideding whether there is an alarm or
// not.
int64_t minObservation_ = 0;
float maxMismatchedRatio_ = 0;
float maxEmptyRatio_ = 0;
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_GATHER_RANGES_TO_DENSE_OPS_H_