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// Copyright 2025 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/permissions/prediction_service/permissions_aiv4_executor.h"
#include <array>
#include <vector>
#include "base/types/optional_ref.h"
#include "components/optimization_guide/core/optimization_guide_util.h"
#include "components/permissions/prediction_service/permissions_aiv4_model_metadata.pb.h"
#include "third_party/tflite_support/src/tensorflow_lite_support/cc/task/core/task_utils.h"
namespace permissions {
using ModelInput = PermissionsAiv4Executor::ModelInput;
using ModelOutput = PermissionsAiv4Executor::ModelOutput;
using ::passage_embeddings::Embedding;
using ::tflite::task::core::PopulateTensor;
// The default size of the text input tensor for the model. This is
// necessary if the model metadata does not provide this value.
constexpr int kDefaultTextInputSize = 768;
PermissionsAiv4ExecutorInput::PermissionsAiv4ExecutorInput(
SkBitmap snapshot,
Embedding inner_text_embedding)
: snapshot(snapshot), inner_text_embedding(inner_text_embedding) {}
PermissionsAiv4ExecutorInput::~PermissionsAiv4ExecutorInput() = default;
PermissionsAiv4ExecutorInput::PermissionsAiv4ExecutorInput(
const PermissionsAiv4ExecutorInput&) = default;
PermissionsAiv4ExecutorInput::PermissionsAiv4ExecutorInput(
PermissionsAiv4ExecutorInput&&) = default;
bool PermissionsAiv4Executor::Preprocess(
const std::vector<TfLiteTensor*>& input_tensors,
const ModelInput& input) {
DCHECK(input_tensors.size() == 2);
int expected_input_size = kDefaultTextInputSize;
if (input.metadata.has_value() &&
input.metadata.value().has_text_embeddings_input_size()) {
expected_input_size = input.metadata.value().text_embeddings_input_size();
}
const auto& embedding = input.inner_text_embedding;
if (static_cast<int>(embedding.Dimensions()) != expected_input_size) {
VLOG(1)
<< "[PermissionsAiv4Executor]: Input Size does not match expectations: "
<< embedding.Dimensions() << " vs (expected) " << expected_input_size;
return false;
}
if (!PopulateTensor<float>(embedding.GetData().data(), expected_input_size,
input_tensors[0])
.ok()) {
VLOG(1) << "[PermissionsAiv4Executor]: Failed to copy passage "
"embedding.";
return false;
}
if (!ConvertSkBitMapToTfliteTensor(input_tensors[1], input.snapshot)) {
VLOG(1)
<< "[PermissionsAiv4Executor]: Failed to convert skbitmap to tflite "
"tensor data.";
return false;
}
VLOG(1) << "[PermissionsAiv4Executor]: Successfully encoded input!";
SetThresholdValues(input.metadata);
return true;
}
void PermissionsAiv4Executor::SetThresholdValues(
base::optional_ref<const PermissionsAiv4ModelMetadata> metadata) {
if (!metadata.has_value() || !metadata.value().has_relevance_thresholds()) {
DCHECK(request_type() == RequestType::kNotifications ||
request_type() == RequestType::kGeolocation);
// Empirically determined thresholds, that map to relevance enum vals as
// follows:
// val < thr[0] -> VeryLow
// ...
// val < thr[4] -> High
// val >= thr[4] -> VeryHigh
relevance_thresholds() = {0.008f, 0.024f, 0.11f, 0.32f};
if (request_type() == RequestType::kGeolocation) {
relevance_thresholds() = {0.033f, 0.077f, 0.2f, 0.49f};
}
return;
}
const auto& thresholds = metadata.value().relevance_thresholds();
relevance_thresholds() = {
thresholds.min_low_relevance(), thresholds.min_medium_relevance(),
thresholds.min_high_relevance(), thresholds.min_very_high_relevance()};
}
} // namespace permissions
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