/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// Contains classes that can execute different models/parts of a model.
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#ifndef LIBTEXTCLASSIFIER_ANNOTATOR_MODEL_EXECUTOR_H_
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#define LIBTEXTCLASSIFIER_ANNOTATOR_MODEL_EXECUTOR_H_
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#include <memory>
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#include "annotator/types.h"
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#include "utils/base/logging.h"
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#include "utils/tensor-view.h"
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#include "utils/tflite-model-executor.h"
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namespace libtextclassifier3 {
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// Executor for the text selection prediction and classification models.
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class ModelExecutor : public TfLiteModelExecutor {
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public:
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static std::unique_ptr<ModelExecutor> FromModelSpec(
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const tflite::Model* model_spec) {
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auto model = TfLiteModelFromModelSpec(model_spec);
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if (!model) {
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return nullptr;
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}
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return std::unique_ptr<ModelExecutor>(new ModelExecutor(std::move(model)));
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}
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static std::unique_ptr<ModelExecutor> FromBuffer(
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const flatbuffers::Vector<uint8_t>* model_spec_buffer) {
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auto model = TfLiteModelFromBuffer(model_spec_buffer);
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if (!model) {
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return nullptr;
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}
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return std::unique_ptr<ModelExecutor>(new ModelExecutor(std::move(model)));
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}
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TensorView<float> ComputeLogits(const TensorView<float>& features,
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tflite::Interpreter* interpreter) const;
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protected:
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explicit ModelExecutor(std::unique_ptr<const tflite::FlatBufferModel> model)
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: TfLiteModelExecutor(std::move(model)) {}
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static const int kInputIndexFeatures = 0;
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static const int kOutputIndexLogits = 0;
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};
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// Executor for embedding sparse features into a dense vector.
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class EmbeddingExecutor {
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public:
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virtual ~EmbeddingExecutor() {}
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// Embeds the sparse_features into a dense embedding and adds (+) it
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// element-wise to the dest vector.
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virtual bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
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int dest_size) const = 0;
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// Returns true when the model is ready to be used, false otherwise.
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virtual bool IsReady() const { return true; }
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};
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class TFLiteEmbeddingExecutor : public EmbeddingExecutor {
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public:
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static std::unique_ptr<TFLiteEmbeddingExecutor> FromBuffer(
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const flatbuffers::Vector<uint8_t>* model_spec_buffer, int embedding_size,
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int quantization_bits,
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const Model_::EmbeddingPruningMask* embedding_pruning_mask = nullptr);
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// Embeds the sparse_features into a dense embedding and adds (+) it
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// element-wise to the dest vector.
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bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
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int dest_size) const;
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// Auxiliary function for computing prefixes used in implementation of
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// efficient mask indexing data structure.
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void ComputePrefixCounts();
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// Function implementing mask indexing based on efficient data structure
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int PruneBucketId(int bucket_id) const;
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protected:
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explicit TFLiteEmbeddingExecutor(
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std::unique_ptr<TfLiteModelExecutor> executor, int quantization_bits,
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int num_buckets, int bytes_per_embedding, int output_embedding_size,
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const TfLiteTensor* scales, const TfLiteTensor* embeddings,
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std::unique_ptr<tflite::Interpreter> interpreter,
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const Model_::EmbeddingPruningMask* embedding_pruning_mask = nullptr);
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std::unique_ptr<TfLiteModelExecutor> executor_;
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int quantization_bits_;
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int num_buckets_ = -1;
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int bytes_per_embedding_ = -1;
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int output_embedding_size_ = -1;
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const TfLiteTensor* scales_ = nullptr;
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const TfLiteTensor* embeddings_ = nullptr;
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// NOTE: This interpreter is used in a read-only way (as a storage for the
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// model params), thus is still thread-safe.
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std::unique_ptr<tflite::Interpreter> interpreter_;
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std::vector<uint64> pruning_mask_;
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std::vector<uint16> prefix_counts_;
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int full_num_buckets_ = -1;
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// Index of row of embedding table corresponding to all pruned buckets.
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int pruned_row_bucket_id_ = -1;
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};
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} // namespace libtextclassifier3
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#endif // LIBTEXTCLASSIFIER_ANNOTATOR_MODEL_EXECUTOR_H_
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