android-10.0.0_r47/search

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/core/framework/dataset.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"

namespace tensorflow {
namespace data {
namespace {

// See documentation in ../../ops/dataset_ops.cc for a high-level description of
// the following op.

class CacheDatasetOp : public UnaryDatasetOpKernel {
 public:
  explicit CacheDatasetOp(OpKernelConstruction* ctx)
      : UnaryDatasetOpKernel(ctx) {}

  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                   DatasetBase** output) override {
    // Parse out the filenames tensor.
    string filename;
    OP_REQUIRES_OK(ctx,
                   ParseScalarArgument<string>(ctx, "filename", &filename));

    if (filename.empty()) {
      *output = new MemoryDataset(ctx, input);
    } else {
      *output = new FileDataset(ctx, input, filename, ctx->env());
    }
  }

 private:
  class FileDataset : public DatasetBase {
   public:
    explicit FileDataset(OpKernelContext* ctx, const DatasetBase* input,
                         string filename, Env* env)
        : DatasetBase(DatasetContext(ctx)),
          input_(input),
          filename_(std::move(filename)),
          env_(env),
          num_tensors_(input->output_dtypes().size()),
          tensor_index_padding_size_(StringPaddingSize(num_tensors_)),
          item_index_padding_size_(StringPaddingSize(kMaxItems)),
          tensor_format_string_(strings::Printf("%%%zuzu_%%%zuzu",
                                                item_index_padding_size_,
                                                tensor_index_padding_size_)) {
      input_->Ref();
      DCHECK_EQ(item_index_padding_size_, 7);
    }

    ~FileDataset() override { input_->Unref(); }

    std::unique_ptr<IteratorBase> MakeIteratorInternal(
        const string& prefix) const override {
      return absl::make_unique<FileIterator>(
          FileIterator::Params{this, strings::StrCat(prefix, "::FileCache")});
    }

    const DataTypeVector& output_dtypes() const override {
      return input_->output_dtypes();
    }

    const std::vector<PartialTensorShape>& output_shapes() const override {
      return input_->output_shapes();
    }

    string DebugString() const override {
      return "CacheDatasetOp::FileDataset";
    }

    int64 Cardinality() const override { return input_->Cardinality(); }

   protected:
    Status AsGraphDefInternal(SerializationContext* ctx,
                              DatasetGraphDefBuilder* b,
                              Node** output) const override {
      Node* input_graph = nullptr;
      TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph));
      Node* filename = nullptr;
      TF_RETURN_IF_ERROR(b->AddScalar(filename_, &filename));
      TF_RETURN_IF_ERROR(b->AddDataset(this, {input_graph, filename}, output));
      return Status::OK();
    }

   private:
    static size_t StringPaddingSize(size_t num_tensors) {
      return strings::Printf("%zu", num_tensors - 1).size();
    }

    string FormatName(size_t item_index, size_t tensor_index) const {
      return strings::Printf(tensor_format_string_.c_str(), item_index,
                             tensor_index);
    }

    class FileIterator : public DatasetIterator<FileDataset> {
     public:
      explicit FileIterator(const Params& params)
          : DatasetIterator<FileDataset>(params) {
        if (params.dataset->env_
                ->FileExists(MetaFilename(params.dataset->filename_))
                .ok()) {
          mode_ = Mode::read;
        } else {
          mode_ = Mode::write;
        }
        InitializeIterator();
      }

      Status Initialize(IteratorContext* ctx) override {
        mutex_lock l(mu_);
        return iterator_->Initialize(ctx);
      }

      Status GetNextInternal(IteratorContext* ctx,
                             std::vector<Tensor>* out_tensors,
                             bool* end_of_sequence) override {
        mutex_lock l(mu_);
        return iterator_->GetNext(ctx, out_tensors, end_of_sequence);
      }

     protected:
      std::shared_ptr<model::Node> CreateNode(
          IteratorContext* ctx, model::Node::Args args) const override {
        return model::MakeKnownRatioNode(std::move(args),
                                         /*ratio=*/1);
      }

      Status SaveInternal(IteratorStateWriter* writer) override {
        mutex_lock l(mu_);
        TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("mode"), mode_));
        return SaveInput(writer, iterator_);
      }
      Status RestoreInternal(IteratorContext* ctx,
                             IteratorStateReader* reader) override {
        mutex_lock l(mu_);
        {
          int64 temp;
          TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("mode"), &temp));
          mode_ = static_cast<Mode>(temp);
        }
        if (mode_ == Mode::write &&
            dataset()
                ->env_->FileExists(MetaFilename(dataset()->filename_))
                .ok()) {
          // This could happen if the cache was completely written after the
          // checkpoint was saved.
          LOG(WARNING)
              << "It looks like the cache was already completely written("
              << MetaFilename(dataset()->filename_)
              << ") after the last checkpoint was saved. "
              << "Attempting to read the cache instead of continuing to "
              << "write. If this is a mistake, please remove the above file "
              << "and try running again.";
          mode_ = Mode::read;
        }
        InitializeIterator();
        TF_RETURN_IF_ERROR(iterator_->Initialize(ctx));
        return RestoreInput(ctx, reader, iterator_);
      }

     private:
      // FileWriterIterator passes through and caches items from the input
      // FileDataset.
      //
      // This iterator is used when the cache directory is not found on disk. It
      // creates the cache directory, and passes on the underlying iterator's
      // elements.
      //
      // Caching is performed by writing the input tensors to disk using the
      // `BundleWriter`. Note that the cache gets fully flushed to disk only
      // after the input iterator has been fully exhausted. If the program
      // exits, before completion of an epoch, the cached state would be lost.
      // To ensure that the partial cache persists across sessions, one should
      // checkpoint the input pipeline. On each call to `SaveInternal` the
      // partial cache gets flushed to disk in files with prefix
      // <filename>_<shard_id> where shard_id is unique for each checkpoint.
      // When all elements have been produced, these shards get coalesced.
      class FileWriterIterator : public DatasetIterator<FileDataset> {
       public:
        explicit FileWriterIterator(const Params& params)
            : DatasetIterator<FileDataset>(params),
              cur_index_(0),
              shard_id_(0),
              filename_(
                  strings::StrCat(params.dataset->filename_, "_", shard_id_)),
              lockfile_(strings::StrCat(filename_, ".lockfile")),
              lockfile_created_(false),
              iteration_completed_(false) {}

        Status Initialize(IteratorContext* ctx) override {
          return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_);
        }

        Status GetNextInternal(IteratorContext* ctx,
                               std::vector<Tensor>* out_tensors,
                               bool* end_of_sequence) override {
          mutex_lock l(mu_);
          TF_RETURN_IF_ERROR(EnsureLockFileExists());
          TF_RETURN_IF_ERROR(writer_->status());
          if (cur_index_ >= kMaxItems) {
            // As a courtesy, close the [truncated] cache file.
            Status s = Finish();
            if (!s.ok()) {
              LOG(ERROR) << s;
            }
            return errors::InvalidArgument(
                "Upstream iterator is producing more than ", kMaxItems,
                " items, which is more than the cache limit.");
          }

          TF_RETURN_IF_ERROR(
              input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
          if (*end_of_sequence && out_tensors->empty()) {
            TF_RETURN_IF_ERROR(Finish());
            cur_index_++;
            return Status::OK();
          }
          if (out_tensors->size() != dataset()->num_tensors_) {
            return errors::Internal(
                "Upstream iterator returned invalid number of tensors. "
                "Expected ",
                dataset()->num_tensors_, " got: ", out_tensors->size());
          }
          size_t tensor_index = 0;
          for (const Tensor& t : *out_tensors) {
            DCHECK_LT(tensor_index, dataset()->num_tensors_);
            string key = dataset()->FormatName(cur_index_, tensor_index++);
            TF_RETURN_IF_ERROR(writer_->Add(key, t));
          }
          if (*end_of_sequence) {
            TF_RETURN_IF_ERROR(Finish());
          }
          cur_index_++;
          return Status::OK();
        }

       protected:
        std::shared_ptr<model::Node> CreateNode(
            IteratorContext* ctx, model::Node::Args args) const override {
          return model::MakeKnownRatioNode(std::move(args),
                                           /*ratio=*/1);
        }

        Status SaveInternal(IteratorStateWriter* writer) override {
          mutex_lock l(mu_);
          if (iteration_completed_) {
            TF_RETURN_IF_ERROR(
                writer->WriteScalar(full_name("iteration_completed"), ""));
            return Status::OK();
          }

          // lockfile is created on the first call to GetNextInternal. The
          // absence of a lockfile means that GetNextInternal was not called
          // and hence nothing was written to cache. So we don't need to worry
          // about flushing the current shard. This ensures that we never write
          // empty shards.
          if (lockfile_created_) {
            // Flush the current bundle.
            TF_RETURN_IF_ERROR(writer_->Finish());

            // Note: We do not delete the lockfile here. We keep lockfiles of
            // all shards around until the entire cache has been written to
            // prevent concurrent iterators from corrupting any of the shards.

            // Start caching to a new shard.
            shard_id_++;
            filename_ = strings::StrCat(dataset()->filename_, "_", shard_id_);
            lockfile_ = strings::StrCat(filename_, ".lockfile");
            lockfile_created_ = false;
          }
          TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
          TF_RETURN_IF_ERROR(
              writer->WriteScalar(full_name("cur_index"), cur_index_));
          TF_RETURN_IF_ERROR(
              writer->WriteScalar(full_name("shard_id"), shard_id_));
          return Status::OK();
        }

        Status RestoreInternal(IteratorContext* ctx,
                               IteratorStateReader* reader) override {
          mutex_lock l(mu_);
          if (reader->Contains(full_name("iteration_completed"))) {
            iteration_completed_ = true;
            return Status::OK();
          }

          TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
          int64 temp;
          // TODO(b/78048575): Update this when saving size_t tensors directly
          // is supported.
          {
            TF_RETURN_IF_ERROR(
                reader->ReadScalar(full_name("cur_index"), &temp));
            cur_index_ = static_cast<size_t>(temp);
            if (cur_index_ != temp) {
              return errors::Internal("Invalid value for cur_index ", temp);
            }
          }
          // TODO(b/78048575): Update this when saving size_t tensors directly
          // is supported.
          {
            TF_RETURN_IF_ERROR(
                reader->ReadScalar(full_name("shard_id"), &temp));
            shard_id_ = static_cast<size_t>(temp);
            if (shard_id_ != temp) {
              return errors::Internal("Invalid value for shard_id ", temp);
            }
          }
          filename_ = strings::StrCat(dataset()->filename_, "_", shard_id_);
          lockfile_ = strings::StrCat(filename_, ".lockfile");
          writer_ = absl::make_unique<BundleWriter>(dataset()->env_, filename_);
          return Status::OK();
        }

       private:
        Status EnsureLockFileExists() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
          if (iteration_completed_)
            return errors::OutOfRange(
                "Attempting to call get_next after iteration should have "
                "finished.");
          if (lockfile_created_ && !iteration_completed_) return Status::OK();

          // Perform rudimentary locking to help catch concurrent writes to the
          // same cache files.

          // 1. Check that a checkpoint for the shard has not already been
          // written.
          if (dataset()->env_->FileExists(MetaFilename(filename_)).ok()) {
            return errors::AlreadyExists("Existing cache files found: \n",
                                         MetaFilename(filename_), "\n",
                                         DataFilename(filename_, 0, 1), "\n",
                                         "To continue delete the above files.");
          }

          // 2. Check that there isn't a concurrent iterator that is writing
          // to cache.
          if (dataset()->env_->FileExists(lockfile_).ok()) {
            // Attempt to read the contents of the lockfile.
            char contents_scratch[151] = {0};  // Initialize all to 0.
            StringPiece contents;
            std::unique_ptr<RandomAccessFile> file;
            if (dataset()->env_->NewRandomAccessFile(lockfile_, &file).ok()) {
              file->Read(0, 150, &contents, contents_scratch).IgnoreError();
            }
            return errors::AlreadyExists(
                "There appears to be a concurrent caching iterator running - "
                "cache lockfile already exists ('",
                lockfile_,
                "'). If you are sure no other running TF computations are "
                "using "
                "this cache prefix, delete the lockfile and re-initialize the "
                "iterator. Lockfile contents: ",
                contents);
          } else {
            // Create the file, and write some basic contents.
            std::unique_ptr<WritableFile> lockfile;
            TF_RETURN_IF_ERROR(
                dataset()->env_->NewWritableFile(lockfile_, &lockfile));
            TF_RETURN_IF_ERROR(lockfile->Append(strings::StrCat(
                "Created at: ", dataset()->env_->NowSeconds())));

            // At this point we know that
            // 1. There is no conflicting checkpoint with prefix `filename_`.
            // 2. There is no concurrent session that is trying to write a ckpt
            //    to filename.
            // So it is safe to create a BundleWriter here. Note that it is
            // unsafe to initialize the BundleWriter anywhere the above
            // conditions are not met since BundleWriter's constructor creates
            // new temp files which can delete the temp files created by a
            // BundleWriter in another Session.
            writer_ =
                absl::make_unique<BundleWriter>(dataset()->env_, filename_);
            lockfile_created_ = true;
            return Status::OK();
          }
        }

        Status Finish() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
          iteration_completed_ = true;
          // Flush the current bundle.
          TF_RETURN_IF_ERROR(writer_->Finish());
          // Merge all the bundles.
          // Currently there are `shard_id_ + 1` bundles, one for each
          // checkpoint. Each bundle has prefix <filename>_<id> where `id` is an
          // integer starting at 0 an incremented by 1 for each new checkpoint.
          // We merge all these bundles into a bundle with prefix <filename> so
          // that the next call to `MakeIterator` can build a
          // `FileReaderIterator`.
          {
            std::vector<string> prefixes;
            prefixes.reserve(shard_id_ + 1);
            for (size_t i = 0; i <= shard_id_; ++i) {
              prefixes.emplace_back(
                  strings::StrCat(dataset()->filename_, "_", i));
            }
            TF_RETURN_IF_ERROR(
                MergeBundles(dataset()->env_, prefixes, dataset()->filename_));
          }
          // Delete all lockfiles.
          for (size_t i = 0; i <= shard_id_; ++i) {
            TF_RETURN_IF_ERROR(dataset()->env_->DeleteFile(
                strings::StrCat(dataset()->filename_, "_", i, ".lockfile")));
          }
          return Status::OK();
        }

        mutex mu_;
        size_t cur_index_ GUARDED_BY(mu_);
        // Index of the current shard. This gets incremented whenever a new
        // cache shard is saved.
        size_t shard_id_ GUARDED_BY(mu_);
        std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_);
        // The current prefix for the cache file. This is equal to
        // `StrCat(dataset()->filename_, "_", shard_id_)`.
        string filename_;
        std::unique_ptr<BundleWriter> writer_ GUARDED_BY(mu_);
        string lockfile_ GUARDED_BY(mu_);
        bool lockfile_created_ GUARDED_BY(mu_);
        bool iteration_completed_ GUARDED_BY(mu_);
      };  // FileWriterIterator

      class FileReaderIterator : public DatasetIterator<FileDataset> {
       public:
        explicit FileReaderIterator(const Params& params)
            : DatasetIterator<FileDataset>(params),
              cur_index_(0),
              reader_(dataset()->env_, dataset()->filename_),
              iterator_restored_(false) {}

        Status GetNextInternal(IteratorContext* ctx,
                               std::vector<Tensor>* out_tensors,
                               bool* end_of_sequence) override {
          mutex_lock l(mu_);
          *end_of_sequence = false;
          TF_RETURN_IF_ERROR(reader_.status());
          if (!reader_.Valid()) {
            return errors::Internal(
                "Cache iterator is in an invalid state. (Perhaps GetNext "
                "called "
                "after end_of_sequence?)");
          }
          out_tensors->clear();
          out_tensors->resize(dataset()->num_tensors_);

          for (size_t i = 0; i < dataset()->num_tensors_; ++i) {
            // When the iterator is restored from the checkpoint, `reader_` is
            // already pointing at `key` so we do not need to skip the header
            // entry.
            if (!iterator_restored_) {
              reader_
                  .Next();  // The first entry in the table is a header entry.
            } else {
              iterator_restored_ = false;
            }
            if (!reader_.Valid()) {
              out_tensors->clear();
              *end_of_sequence = true;
              return Status::OK();
            }
            StringPiece key = reader_.key();
            DCHECK_EQ(key, dataset()->FormatName(cur_index_, i));
            TF_RETURN_IF_ERROR(reader_.ReadCurrent(&(*out_tensors)[i]));
            TF_RETURN_IF_ERROR(reader_.status());
          }
          cur_index_++;
          return Status::OK();
        }

       protected:
        std::shared_ptr<model::Node> CreateNode(
            IteratorContext* ctx, model::Node::Args args) const override {
          return model::MakeKnownRatioNode(std::move(args),
                                           /*ratio=*/1);
        }

        Status SaveInternal(IteratorStateWriter* writer) override {
          mutex_lock l(mu_);
          TF_RETURN_IF_ERROR(
              writer->WriteScalar(full_name("cur_index"), cur_index_));
          return Status::OK();
        }

        Status RestoreInternal(
            IteratorContext* ctx,
            IteratorStateReader* iterator_state_reader) override {
          mutex_lock l(mu_);
          {
            // TODO(b/78048575): Update this when saving size_t tensors directly
            // is supported.
            int64 temp;
            TF_RETURN_IF_ERROR(iterator_state_reader->ReadScalar(
                full_name("cur_index"), &temp));
            cur_index_ = static_cast<size_t>(temp);
            if (cur_index_ != temp) {
              return errors::Internal("Invalid value for cur_index ", temp);
            }
          }
          if (!reader_.Valid()) {
            return errors::Internal("Error initializing BundleReader.");
          }
          reader_.Seek(dataset()->FormatName(cur_index_, 0));
          iterator_restored_ = true;
          return Status::OK();
        }

       private:
        mutex mu_;
        size_t cur_index_ GUARDED_BY(mu_);
        BundleReader reader_ GUARDED_BY(mu_);
        bool iterator_restored_ GUARDED_BY(mu_);
      };  // FileReaderIterator

      void InitializeIterator() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
        // We intentionally use the same prefix for both `FileReaderIterator`
        // and `FileWriterIterator`. Since at any time there will be at most
        // one of them alive, there should be no conflicts. This allows both
        // iterators to use a common key for `cur_index`. We leverage this
        // in the corner case when this iterator is restored from an old
        // checkpoint in `write` mode and the cache has been completely
        // flushed to disk since then. In that case we simply build a
        // `FileReaderIterator` and seek to the `cur_index`.
        switch (mode_) {
          case Mode::read:
            iterator_ = absl::make_unique<FileReaderIterator>(
                FileReaderIterator::Params{dataset(),
                                           strings::StrCat(prefix(), "Impl")});
            break;
          case Mode::write:
            iterator_ = absl::make_unique<FileWriterIterator>(
                FileWriterIterator::Params{dataset(),
                                           strings::StrCat(prefix(), "Impl")});
        }
      }

      mutex mu_;
      enum Mode { read, write };
      Mode mode_ GUARDED_BY(mu_);
      std::unique_ptr<IteratorBase> iterator_ GUARDED_BY(mu_);
    };  // FileIterator

    const DatasetBase* const input_;
    const string filename_;
    Env* const env_;
    const size_t num_tensors_;
    const size_t tensor_index_padding_size_;
    static const size_t kMaxItems = 10000000;  // 10 million
    const size_t item_index_padding_size_;
    const string tensor_format_string_;
  };  // FileDataset

  class MemoryDataset : public DatasetBase {
   public:
    explicit MemoryDataset(OpKernelContext* ctx, const DatasetBase* input)
        : DatasetBase(DatasetContext(ctx)), input_(input) {
      input->Ref();
    }

    ~MemoryDataset() override { input_->Unref(); }

    std::unique_ptr<IteratorBase> MakeIteratorInternal(
        const string& prefix) const override {
      return absl::make_unique<MemoryIterator>(MemoryIterator::Params{
          this, strings::StrCat(prefix, "::MemoryCache")});
    }

    const DataTypeVector& output_dtypes() const override {
      return input_->output_dtypes();
    }

    const std::vector<PartialTensorShape>& output_shapes() const override {
      return input_->output_shapes();
    }

    string DebugString() const override {
      return "CacheDatasetOp::MemoryDataset";
    }

    int64 Cardinality() const override { return input_->Cardinality(); }

   protected:
    Status AsGraphDefInternal(SerializationContext* ctx,
                              DatasetGraphDefBuilder* b,
                              Node** output) const override {
      Node* input_node = nullptr;
      TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_node));
      Node* filename_node = nullptr;
      TF_RETURN_IF_ERROR(b->AddScalar(string(""), &filename_node));
      TF_RETURN_IF_ERROR(
          b->AddDataset(this, {input_node, filename_node}, output));
      return Status::OK();
    }

   private:
    // A thread-safe data structure for caching dataset elements.
    //
    // The expected use is that a single `MemoryWriterIterator` populates the
    // cache with dataset elements. Once all elements are cached, the cache can
    // be used by one or more `MemoryReaderIterator`s.
    class MemoryCache : public ResourceBase {
     public:
      MemoryCache() = default;

      string DebugString() const override {
        return "CacheDataset::MemoryCache";
      }

      // Marks the cache as completed.
      void Complete() {
        mutex_lock l(mu_);
        completed_ = true;
      }

      // Returns whether the cache is claimed.
      bool IsClaimed() {
        tf_shared_lock l(mu_);
        return claimed_;
      }

      // Returns whether the cache is completed.
      bool IsCompleted() {
        tf_shared_lock l(mu_);
        return completed_;
      }

      // Attempts to claim the cache, returning whether the cache was claimed.
      bool MaybeClaim() {
        mutex_lock l(mu_);
        if (!claimed_) {
          claimed_ = true;
          return true;
        }
        return false;
      }

      // Resets the cache.
      void Reset() {
        mutex_lock l(mu_);
        claimed_ = false;
        completed_ = false;
        cache_.clear();
      }

      // Returns the element at the given index.
      const std::vector<Tensor>& at(int64 index) {
        tf_shared_lock l(mu_);
        DCHECK(index < cache_.size());
        return cache_[index];
      }

      // Adds the element to the cache.
      void emplace_back(std::vector<Tensor> element) {
        mutex_lock l(mu_);
        cache_.emplace_back(std::move(element));
      }

      // Returns the size of the cache.
      size_t size() {
        tf_shared_lock l(mu_);
        return cache_.size();
      }

     private:
      mutex mu_;
      // Determines whether a writer has claimed the cache.
      bool claimed_ GUARDED_BY(mu_) = false;
      // Determines whether all elements of the dataset have been cached.
      bool completed_ GUARDED_BY(mu_) = false;
      std::vector<std::vector<Tensor>> cache_ GUARDED_BY(mu_);
    };

    class MemoryIterator : public DatasetIterator<MemoryDataset> {
     public:
      explicit MemoryIterator(const Params& params)
          : DatasetIterator<MemoryDataset>(params) {}

      ~MemoryIterator() override { cache_->Unref(); }

      Status Initialize(IteratorContext* ctx) override {
        mutex_lock l(mu_);
        // Use the resource manager in the iterator context to get / create
        // a cache.
        ResourceMgr* mgr = ctx->resource_mgr();
        const string name = strings::StrCat(
            prefix(), "::", dataset()->node_name(), "::MemoryCache");
        TF_RETURN_IF_ERROR(mgr->LookupOrCreate<MemoryCache>(
            "tf_data", name, &cache_, [](MemoryCache** cache) {
              *cache = new MemoryCache();
              return Status::OK();
            }));
        mode_ = cache_->MaybeClaim() ? Mode::write : Mode::read;
        InitializeIterator();
        if (mode_ == Mode::read && !cache_->IsCompleted()) {
          return errors::Internal(
              "Cache should only be read after it has been completed.");
        }
        return iterator_->Initialize(ctx);
      }

      Status GetNextInternal(IteratorContext* ctx,
                             std::vector<Tensor>* out_tensors,
                             bool* end_of_sequence) override {
        mutex_lock l(mu_);
        return iterator_->GetNext(ctx, out_tensors, end_of_sequence);
      }

     protected:
      std::shared_ptr<model::Node> CreateNode(
          IteratorContext* ctx, model::Node::Args args) const override {
        return model::MakeKnownRatioNode(std::move(args),
                                         /*ratio=*/1);
      }

      Status SaveInternal(IteratorStateWriter* writer) override {
        mutex_lock l(mu_);
        TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("mode"), mode_));
        if (cache_->IsClaimed()) {
          TF_RETURN_IF_ERROR(
              writer->WriteScalar(full_name("cache_claimed"), ""));
          size_t cache_size = cache_->size();
          TF_RETURN_IF_ERROR(
              writer->WriteScalar(full_name("cache_size"), cache_size));
          for (size_t i = 0; i < cache_size; i++) {
            auto& element = cache_->at(i);
            TF_RETURN_IF_ERROR(writer->WriteScalar(
                full_name(strings::StrCat("cache[", i, "].size")),
                element.size()));
            for (size_t j = 0; j < element.size(); ++j) {
              TF_RETURN_IF_ERROR(writer->WriteTensor(
                  full_name(strings::StrCat("cache[", i, "][", j, "]")),
                  element[j]));
            }
          }
          if (cache_->IsCompleted()) {
            TF_RETURN_IF_ERROR(
                writer->WriteScalar(full_name("cache_completed"), ""));
          }
        }
        return SaveInput(writer, iterator_);
      }

      Status RestoreInternal(IteratorContext* ctx,
                             IteratorStateReader* reader) override {
        mutex_lock l(mu_);
        iterator_.reset();
        cache_->Reset();
        {
          int64 temp;
          TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("mode"), &temp));
          mode_ = static_cast<Mode>(temp);
        }
        if (reader->Contains(full_name("cache_claimed"))) {
          CHECK(cache_->MaybeClaim());
          size_t cache_size;
          {
            int64 temp;
            TF_RETURN_IF_ERROR(
                reader->ReadScalar(full_name("cache_size"), &temp));
            cache_size = static_cast<size_t>(temp);
          }
          for (size_t i = 0; i < cache_size; ++i) {
            std::vector<Tensor> element;
            size_t element_size;
            {
              int64 temp;
              TF_RETURN_IF_ERROR(reader->ReadScalar(
                  full_name(strings::StrCat("cache[", i, "].size")), &temp));
              element_size = static_cast<size_t>(temp);
            }
            element.reserve(element_size);
            for (size_t j = 0; j < element_size; ++j) {
              element.emplace_back();
              TF_RETURN_IF_ERROR(reader->ReadTensor(
                  full_name(strings::StrCat("cache[", i, "][", j, "]")),
                  &element.back()));
            }
            cache_->emplace_back(std::move(element));
          }
          if (reader->Contains(full_name("cache_completed"))) {
            cache_->Complete();
          }
        }
        InitializeIterator();
        TF_RETURN_IF_ERROR(iterator_->Initialize(ctx));
        return RestoreInput(ctx, reader, iterator_);
      }

     private:
      class MemoryWriterIterator : public DatasetIterator<MemoryDataset> {
       public:
        explicit MemoryWriterIterator(const Params& params, MemoryCache* cache)
            : DatasetIterator<MemoryDataset>(params), cache_(cache) {
          CHECK(cache_);
        }

        ~MemoryWriterIterator() override {
          mutex_lock l(mu_);
          if (cache_->size() > 0 && !cache_->IsCompleted()) {
            LOG(WARNING)
                << "The calling iterator did not fully read the dataset being "
                   "cached. In order to avoid unexpected truncation of the "
                   "dataset, the partially cached contents of the dataset "
                   "will be discarded. This can happen if you have an input "
                   "pipeline similar to `dataset.cache().take(k).repeat()`. "
                   "You should use `dataset.take(k).cache().repeat()` instead.";
            cache_->Reset();
          }
        }

        Status Initialize(IteratorContext* ctx) override {
          return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_);
        }

        Status GetNextInternal(IteratorContext* ctx,
                               std::vector<Tensor>* out_tensors,
                               bool* end_of_sequence) override {
          mutex_lock l(mu_);
          TF_RETURN_IF_ERROR(
              input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
          if (*end_of_sequence) {
            cache_->Complete();
            return Status::OK();
          }
          RecordBufferEnqueue(ctx, *out_tensors);
          cache_->emplace_back(*out_tensors);
          return Status::OK();
        }

       protected:
        std::shared_ptr<model::Node> CreateNode(
            IteratorContext* ctx, model::Node::Args args) const override {
          return model::MakeKnownRatioNode(std::move(args),
                                           /*ratio=*/1);
        }

        Status SaveInternal(IteratorStateWriter* writer) override {
          mutex_lock l(mu_);
          return SaveInput(writer, input_impl_);
        }

        Status RestoreInternal(IteratorContext* ctx,
                               IteratorStateReader* reader) override {
          mutex_lock l(mu_);
          return RestoreInput(ctx, reader, input_impl_);
        }

       private:
        mutex mu_;
        std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_);
        MemoryCache* const cache_ GUARDED_BY(mu_);  // not owned.
      };  // MemoryWriterIterator

      class MemoryReaderIterator : public DatasetIterator<MemoryDataset> {
       public:
        explicit MemoryReaderIterator(const Params& params, MemoryCache* cache)
            : DatasetIterator<MemoryDataset>(params), cache_(cache), index_(0) {
          CHECK(cache);
        }

        Status Initialize(IteratorContext* ctx) override {
          // The memory allocated for the cache is owned by the parent
          // dataset but performance modeling uses the iterator abstraction and
          // thus we record the memory allocated for the cache here. The caveat
          // is that this is incorrect if there are concurrent instances of this
          // iterator.
          tf_shared_lock l(mu_);
          for (size_t i = 0; i < cache_->size(); ++i) {
            RecordBufferEnqueue(ctx, cache_->at(i));
          }
          return Status::OK();
        }

        Status GetNextInternal(IteratorContext* ctx,
                               std::vector<Tensor>* out_tensors,
                               bool* end_of_sequence) override {
          mutex_lock l(mu_);
          if (index_ < cache_->size()) {
            const std::vector<Tensor>& cache_tensors = cache_->at(index_);
            out_tensors->insert(out_tensors->begin(), cache_tensors.begin(),
                                cache_tensors.end());
            index_++;
            *end_of_sequence = false;
            return Status::OK();
          } else {
            *end_of_sequence = true;
            return Status::OK();
          }
        }

       protected:
        std::shared_ptr<model::Node> CreateNode(
            IteratorContext* ctx, model::Node::Args args) const override {
          return model::MakeKnownRatioNode(std::move(args),
                                           /*ratio=*/1);
        }

        Status SaveInternal(IteratorStateWriter* writer) override {
          mutex_lock l(mu_);
          TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("index"), index_));
          return Status::OK();
        }

        Status RestoreInternal(IteratorContext* ctx,
                               IteratorStateReader* reader) override {
          mutex_lock l(mu_);
          {
            int64 temp;
            TF_RETURN_IF_ERROR(reader->ReadScalar(full_name("index"), &temp));
            index_ = static_cast<size_t>(temp);
          }
          return Status::OK();
        }

       private:
        mutex mu_;
        MemoryCache* const cache_ GUARDED_BY(mu_);  // not owned.
        size_t index_ GUARDED_BY(mu_);
      };  // MemoryReaderIterator

      void InitializeIterator() EXCLUSIVE_LOCKS_REQUIRED(mu_) {
        switch (mode_) {
          case Mode::read:
            iterator_ = absl::make_unique<MemoryReaderIterator>(
                MemoryReaderIterator::Params{dataset(),
                                             strings::StrCat(prefix(), "Impl")},
                cache_);
            break;
          case Mode::write:
            iterator_ = absl::make_unique<MemoryWriterIterator>(
                MemoryWriterIterator::Params{dataset(),
                                             strings::StrCat(prefix(), "Impl")},
                cache_);
        }
      }

      mutex mu_;
      MemoryCache* cache_ GUARDED_BY(mu_);  // not owned.
      enum Mode { read, write };
      Mode mode_ GUARDED_BY(mu_);
      std::unique_ptr<IteratorBase> iterator_ GUARDED_BY(mu_);
    };  // MemoryIterator

    const DatasetBase* const input_;
  };  // MemoryDataset
};    // CacheDatasetOp

REGISTER_KERNEL_BUILDER(Name("CacheDataset").Device(DEVICE_CPU),
                        CacheDatasetOp);

}  // namespace
}  // namespace data
}  // namespace tensorflow