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// Copyright 2022 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "components/segmentation_platform/internal/execution/processing/feature_processor_state.h"
#include "base/strings/string_number_conversions.h"
#include "base/task/sequenced_task_runner.h"
#include "base/time/time.h"
#include "components/segmentation_platform/internal/database/ukm_types.h"
#include "components/segmentation_platform/internal/metadata/metadata_utils.h"
#include "components/segmentation_platform/internal/stats.h"
#include "components/segmentation_platform/public/config.h"
namespace segmentation_platform::processing {
FeatureProcessorState::FeatureProcessorState()
: prediction_time_(base::Time::Now()),
bucket_duration_(base::TimeDelta()),
segment_id_(SegmentId::OPTIMIZATION_TARGET_UNKNOWN) {}
FeatureProcessorState::FeatureProcessorState(
FeatureProcessorStateId id,
base::Time prediction_time,
base::Time observation_time,
base::TimeDelta bucket_duration,
SegmentId segment_id,
scoped_refptr<InputContext> input_context,
FeatureListQueryProcessor::FeatureProcessorCallback callback)
: id_(id),
prediction_time_(prediction_time),
observation_time_(observation_time),
bucket_duration_(bucket_duration),
segment_id_(segment_id),
input_context_(std::move(input_context)),
callback_(std::move(callback)) {}
FeatureProcessorState::~FeatureProcessorState() = default;
void FeatureProcessorState::SetError(stats::FeatureProcessingError error,
const std::string& message) {
stats::RecordFeatureProcessingError(segment_id_, error);
LOG(ERROR) << "Processing error occured: model "
<< SegmentIdToHistogramVariant(segment_id_) << " failed with "
<< stats::FeatureProcessingErrorToString(error)
<< ", message: " << message;
error_ = true;
input_tensor_.clear();
}
base::WeakPtr<FeatureProcessorState> FeatureProcessorState::GetWeakPtr() {
return weak_ptr_factory_.GetWeakPtr();
}
std::optional<std::pair<std::unique_ptr<QueryProcessor>, bool>>
FeatureProcessorState::PopNextProcessor() {
std::optional<std::pair<std::unique_ptr<QueryProcessor>, bool>>
next_processor;
if (!out_processors_.empty()) {
std::unique_ptr<QueryProcessor> processor =
std::move(out_processors_.front());
out_processors_.pop_front();
next_processor = std::make_pair(std::move(processor), false);
} else if (!in_processors_.empty()) {
std::unique_ptr<QueryProcessor> processor =
std::move(in_processors_.front());
in_processors_.pop_front();
next_processor = std::make_pair(std::move(processor), true);
}
return next_processor;
}
void FeatureProcessorState::AppendProcessor(
std::unique_ptr<QueryProcessor> processor,
bool is_input) {
if (is_input) {
in_processors_.emplace_back(std::move(processor));
} else {
out_processors_.emplace_back(std::move(processor));
}
}
void FeatureProcessorState::AppendIndexedTensors(
const QueryProcessor::IndexedTensors& result,
bool is_input) {
if (is_input) {
for (const auto& item : result) {
input_tensor_[item.first] = item.second;
}
} else {
for (const auto& item : result) {
output_tensor_[item.first] = item.second;
}
}
}
void FeatureProcessorState::OnFinishProcessing() {
std::vector<float> input;
std::vector<float> output;
if (!error_) {
input = MergeTensors(std::move(input_tensor_));
output = MergeTensors(std::move(output_tensor_));
stats::RecordFeatureProcessingError(
segment_id_, stats::FeatureProcessingError::kSuccess);
}
base::SequencedTaskRunner::GetCurrentDefault()->PostTask(
FROM_HERE, base::BindOnce(std::move(callback_), error_, std::move(input),
std::move(output)));
}
std::vector<float> FeatureProcessorState::MergeTensors(
const QueryProcessor::IndexedTensors& tensor) {
std::vector<float> result;
if (metadata_utils::ValidateIndexedTensors(tensor, tensor.size()) !=
metadata_utils::ValidationResult::kValidationSuccess) {
// Note that since the state does not know the expected size, if a tensor is
// missing from the end of the indexed tensor, this validation will not
// fail.
SetError(stats::FeatureProcessingError::kResultTensorError);
} else {
for (size_t i = 0; i < tensor.size(); ++i) {
for (const ProcessedValue& value : tensor.at(i)) {
if (value.type == ProcessedValue::Type::FLOAT) {
result.push_back(value.float_val);
} else {
SetError(stats::FeatureProcessingError::kResultTensorError,
"Expected ProcessedValue::Type::FLOAT but found " +
base::NumberToString(static_cast<int>(value.type)));
return result;
}
}
}
}
return result;
}
} // namespace segmentation_platform::processing
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