EVOLUTION-MANAGER

Edit File: initializable_lookup_table.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_INITIALIZABLE_LOOKUP_TABLE_H_
#define TENSORFLOW_CORE_KERNELS_INITIALIZABLE_LOOKUP_TABLE_H_

#include <atomic>

#include "tensorflow/core/framework/lookup_interface.h"
#include "tensorflow/core/platform/macros.h"

namespace tensorflow {
namespace lookup {

// Base class for lookup tables that require initialization.
class InitializableLookupTable : public LookupInterface {
 public:
  class InitTableIterator;

// Performs batch lookups, for every element in the key tensor, Find returns
  // the corresponding value into the values tensor.
  // If an element is not present in the table, the given default value is used.
  //
  // For tables that require initialization, `Find` is available once the table
  // is marked as initialized.
  //
  // Returns the following statuses:
  // - OK: when the find finishes successfully.
  // - FailedPrecondition: if the table is not initialized.
  // - InvalidArgument: if any of the preconditions on the lookup key or value
  //   fails.
  // - In addition, other implementations may provide another non-OK status
  //   specific to their failure modes.
  Status Find(OpKernelContext* ctx, const Tensor& keys, Tensor* values,
              const Tensor& default_value) final;

// Returns errors::Unimplemented.
  Status Insert(OpKernelContext* ctx, const Tensor& keys,
                const Tensor& values) final {
    return errors::Unimplemented(
        "Insert not supported by InitializableLookupTable implementations");
  }

// Returns errors::Unimplemented.
  Status Remove(OpKernelContext* ctx, const Tensor& keys) final {
    return errors::Unimplemented(
        "Remove not supported by InitializableLookupTable implementations");
  }

Status ExportValues(OpKernelContext* context) override {
    return errors::Unimplemented(
        "ExportValues not supported by InitializableLookupTable "
        "implementations");
  }

Status ImportValues(OpKernelContext* ctx, const Tensor& keys,
                      const Tensor& values) final;

TensorShape key_shape() const final { return TensorShape(); }

TensorShape value_shape() const final { return TensorShape(); }

// Returns whether the table was initialized and is ready to serve lookups.
  bool is_initialized() const {
    return is_initialized_.load(std::memory_order_acquire);
  }

// Initializes the table from the given init table iterator.
  //
  // Atomically, this operation prepares the table, populates it with the given
  // iterator, and mark the table as initialized.
  //
  // Returns the following statuses:
  // - OK: when the initialization was successful.
  // - InvalidArgument: if any of the preconditions on the lookup key or value
  //   fails.
  // - FailedPrecondition: if the table is already initialized and
  //   fail_if_initialized is set to true.
  // - In addition, other implementations may provide another non-OK status
  //   specific to their failure modes.
  Status Initialize(InitTableIterator& iter);

// Basic iterator to initialize lookup tables.
  // It yields a sequence of pairs of `keys()` and `values()` Tensors, so that
  // the consumer may insert key-value pairs in batches.
  //
  // Then the iterator is exhausted, valid returns false and status returns
  // Status::OutOfRange.
  //
  // This class is Thread-unsafe.
  class InitTableIterator {
   public:
    InitTableIterator() {}

virtual ~InitTableIterator() {}

// Prepares the next batch of key and value tensors.
    virtual void Next() = 0;

// Returns true if keys and values point to valid tensors.
    virtual bool Valid() const = 0;

// Returns a tensor that contains the current batch of 'key' values.
    virtual const Tensor& keys() const = 0;

// Returns a tensor that contains the current batch of 'value' values.
    virtual const Tensor& values() const = 0;

// Returns an error if one has occurred, otherwise returns Status::OK.
    virtual Status status() const = 0;

// Returns the total number of elements that the iterator will produce.
    // It might return -1 in case of error.
    virtual int64 total_size() const = 0;

private:
    TF_DISALLOW_COPY_AND_ASSIGN(InitTableIterator);
  };

InitializableLookupTable* GetInitializableLookupTable() override {
    return this;
  }

protected:
  // Prepares and allocates the underlying data structure to store the given
  // number of expected elements.
  virtual Status DoPrepare(size_t expected_num_elements) = 0;

// Same as DoPrepare() but derived implementations might choose to skip
  // calling get_expected_num_elements if size is not needed for DoPrepare.
  virtual Status DoLazyPrepare(
      std::function<int64(void)> get_expected_num_elements) {
    int64 expected_num_elements = get_expected_num_elements();
    if (expected_num_elements < 0) {
      return errors::FailedPrecondition("Got negative expected_num_elements.");
    }
    return DoPrepare(expected_num_elements);
  }

// Populates the table in batches given keys and values as tensors into the
  // underlying data structure.
  virtual Status DoInsert(const Tensor& keys, const Tensor& values) = 0;

// Performs the batch find operation on the underlying data structure.
  virtual Status DoFind(const Tensor& keys, Tensor* values,
                        const Tensor& default_value) = 0;

virtual Status AreEntriesSame(const InitTableIterator& iter, bool* result);

mutex mu_;

private:
  std::atomic<bool> is_initialized_{false};
};

// Iterator to initialize tables given 'keys' and 'values' tensors.
//
// The two tensors are returned in the first iteration. It doesn't loop
// over each element of the tensor since insertions in the lookup table can
// process batches.
class KeyValueTensorIterator
    : public InitializableLookupTable::InitTableIterator {
 public:
  // keys and values are not owned by the iterator.
  explicit KeyValueTensorIterator(const Tensor* keys, const Tensor* values)
      : keys_(keys), values_(values), valid_(true), status_(Status::OK()) {
    TensorShape key_shape = keys_->shape();
    if (!key_shape.IsSameSize(values_->shape())) {
      valid_ = false;
      status_ = errors::InvalidArgument(
          "keys and values should have the same dimension.",
          key_shape.DebugString(), " vs ", values_->shape().DebugString());
    }
    if (key_shape.num_elements() == 0) {
      valid_ = false;
      status_ =
          errors::InvalidArgument("keys and values cannot be empty tensors.");
    }
  }

bool Valid() const override { return valid_; }

void Next() override {
    valid_ = false;
    status_ = errors::OutOfRange("No more data.");
  }

const Tensor& keys() const override { return *keys_; }

const Tensor& values() const override { return *values_; }

Status status() const override { return status_; }

int64 total_size() const override {
    return keys_ == nullptr ? -1 : keys_->NumElements();
  }

private:
  TF_DISALLOW_COPY_AND_ASSIGN(KeyValueTensorIterator);

const Tensor* keys_;    // Doesn't own it.
  const Tensor* values_;  // Doesn't own it.
  bool valid_;            // true if the iterator points to an existing range.
  Status status_;
};

}  // namespace lookup
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_INITIALIZABLE_LOOKUP_TABLE_H_