xref: /aosp_15_r20/external/tensorflow/tensorflow/core/kernels/data/repeat_dataset_op.cc (revision b6fb3261f9314811a0f4371741dbb8839866f948)

/* 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/kernels/data/repeat_dataset_op.h"

#include <utility>

#include "tensorflow/core/data/name_utils.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {
namespace data {

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

/* static */ constexpr const char* const RepeatDatasetOp::kDatasetType;
/* static */ constexpr const char* const RepeatDatasetOp::kInputDataset;
/* static */ constexpr const char* const RepeatDatasetOp::kCount;
/* static */ constexpr const char* const RepeatDatasetOp::kOutputTypes;
/* static */ constexpr const char* const RepeatDatasetOp::kOutputShapes;

constexpr char kForeverRepeat[] = "ForeverRepeat";
constexpr char kEmptyRepeat[] = "EmptyRepeat";
constexpr char kFiniteRepeat[] = "FiniteRepeat";
constexpr char kCurIteration[] = "i";
constexpr char kInputImplEmpty[] = "input_impl_empty";
constexpr char kUninitialized[] = "uninitialized";
constexpr int64_t kKnownRatio = 1;

class RepeatDatasetOp::Dataset : public DatasetBase {
 public:
  Dataset(OpKernelContext* ctx, int64_t count, const DatasetBase* input)
      : DatasetBase(DatasetContext(ctx)), count_(count), input_(input) {
    input_->Ref();
  }

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

  std::unique_ptr<IteratorBase> MakeIteratorInternal(
      const string& prefix) const override {
    if (count_ < 0) {
      return std::make_unique<ForeverIterator>(ForeverIterator::Params{
          this, name_utils::IteratorPrefix(kForeverRepeat, prefix)});
    } else if (count_ == 0) {
      return std::make_unique<EmptyIterator>(EmptyIterator::Params{
          this, name_utils::IteratorPrefix(kEmptyRepeat, prefix)});
    } else {
      return std::make_unique<FiniteIterator>(FiniteIterator::Params{
          this, name_utils::IteratorPrefix(kFiniteRepeat, prefix)});
    }
  }

  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 name_utils::DatasetDebugString(RepeatDatasetOp::kDatasetType);
  }

  int64_t CardinalityInternal() const override {
    int64_t n = input_->Cardinality();
    if (count_ < 0) {
      if (n == 0) {
        return 0;
      }
      return kInfiniteCardinality;
    }
    if (count_ == 0) {
      return 0;
    }
    if (n == kInfiniteCardinality || n == kUnknownCardinality) {
      return n;
    }
    return count_ * n;
  }

  int64_t CardinalityInternal(CardinalityOptions options) const override {
    int64_t n = input_->Cardinality(options);
    if (count_ < 0) {
      if (n == 0) {
        return 0;
      }
      return kInfiniteCardinality;
    }
    if (count_ == 0) {
      return 0;
    }
    if (n == kInfiniteCardinality || n == kUnknownCardinality) {
      return n;
    }
    return count_ * n;
  }

  Status InputDatasets(std::vector<const DatasetBase*>* inputs) const override {
    inputs->push_back(input_);
    return OkStatus();
  }

  Status CheckExternalState() const override {
    return input_->CheckExternalState();
  }

  Status Get(OpKernelContext* ctx, int64 index,
             std::vector<Tensor>* out_tensors) const override {
    TF_RETURN_IF_ERROR(CheckRandomAccessCompatible(index));
    return input_->Get(ctx, index % input_->Cardinality(), out_tensors);
  }

 protected:
  Status AsGraphDefInternal(SerializationContext* ctx,
                            DatasetGraphDefBuilder* b,
                            Node** output) const override {
    Node* input_graph_node = nullptr;
    TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node));
    Node* count = nullptr;
    TF_RETURN_IF_ERROR(b->AddScalar(count_, &count));
    TF_RETURN_IF_ERROR(b->AddDataset(this, {input_graph_node, count}, output));
    return OkStatus();
  }

 private:
  class EmptyIterator : public DatasetIterator<Dataset> {
   public:
    explicit EmptyIterator(const Params& params)
        : DatasetIterator<Dataset>(params) {}
    Status GetNextInternal(IteratorContext* ctx,
                           std::vector<Tensor>* out_tensors,
                           bool* end_of_sequence) override {
      *end_of_sequence = true;
      return OkStatus();
    }

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

    Status SaveInternal(SerializationContext* ctx,
                        IteratorStateWriter* writer) override {
      return OkStatus();
    }
    Status RestoreInternal(IteratorContext* ctx,
                           IteratorStateReader* reader) override {
      return OkStatus();
    }
  };

  class FiniteIterator : public DatasetIterator<Dataset> {
   public:
    explicit FiniteIterator(const Params& params)
        : DatasetIterator<Dataset>(params), i_(0) {}

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

    Status GetNextInternal(IteratorContext* ctx,
                           std::vector<Tensor>* out_tensors,
                           bool* end_of_sequence) override {
      mutex_lock l(mu_);  // TODO(mrry): Make locking less conservative.
      if (!input_impl_) {
        *end_of_sequence = true;
        return OkStatus();
      }
      while (i_ < dataset()->count_) {
        TF_RETURN_IF_ERROR(
            input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
        if (!*end_of_sequence) {
          return OkStatus();
        }
        ++i_;
        for (const auto& provider : ctx->split_providers()) {
          TF_RETURN_IF_ERROR(provider->Reset());
        }
        TF_RETURN_IF_ERROR(
            dataset()->input_->MakeIterator(ctx, this, prefix(), &input_impl_));
      }
      *end_of_sequence = true;
      input_impl_.reset();
      return OkStatus();
    }

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

    Status SaveInternal(SerializationContext* ctx,
                        IteratorStateWriter* writer) override {
      mutex_lock l(mu_);
      TF_RETURN_IF_ERROR(writer->WriteScalar(full_name(kCurIteration), i_));
      if (!input_impl_) {
        TF_RETURN_IF_ERROR(writer->WriteScalar(full_name(kInputImplEmpty), ""));
      } else {
        TF_RETURN_IF_ERROR(SaveInput(ctx, writer, input_impl_));
      }
      return OkStatus();
    }

    Status RestoreInternal(IteratorContext* ctx,
                           IteratorStateReader* reader) override {
      mutex_lock l(mu_);
      TF_RETURN_IF_ERROR(reader->ReadScalar(full_name(kCurIteration), &i_));
      if (!reader->Contains(full_name(kInputImplEmpty))) {
        TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
      } else {
        input_impl_.reset();
      }
      return OkStatus();
    }

   private:
    mutex mu_;
    int64_t i_ TF_GUARDED_BY(mu_);
    std::unique_ptr<IteratorBase> input_impl_ TF_GUARDED_BY(mu_);
  };

  class ForeverIterator : public DatasetIterator<Dataset> {
   public:
    explicit ForeverIterator(const Params& params)
        : DatasetIterator<Dataset>(params),
          input_impl_(nullptr),
          first_call_(true) {}

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

    Status GetNextInternal(IteratorContext* ctx,
                           std::vector<Tensor>* out_tensors,
                           bool* end_of_sequence) override {
      mutex_lock l(mu_);  // TODO(mrry): Make locking less conservative.
      do {
        if (!input_impl_) {
          TF_RETURN_IF_ERROR(dataset()->input_->MakeIterator(
              ctx, this, prefix(), &input_impl_));
        }
        TF_RETURN_IF_ERROR(
            input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
        DCHECK(!*end_of_sequence || out_tensors->empty());
        if (first_call_ && *end_of_sequence && ctx->split_providers().empty()) {
          // If the first call to GetNext() fails because the end of sequence
          // has been reached, we terminate the iteration immediately.
          // Otherwise, this iterator would loop infinitely and never produce a
          // value.
          input_impl_.reset();
          return OkStatus();
        }
        first_call_ = false;
        if (!*end_of_sequence) {
          return OkStatus();
        }
        for (const auto& provider : ctx->split_providers()) {
          TF_RETURN_IF_ERROR(provider->Reset());
        }
        input_impl_.reset();
        first_call_ = true;
      } while (true);
    }

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

    Status SaveInternal(SerializationContext* ctx,
                        IteratorStateWriter* writer) override {
      mutex_lock l(mu_);
      if (!first_call_)
        TF_RETURN_IF_ERROR(SaveInput(ctx, writer, input_impl_));
      else
        TF_RETURN_IF_ERROR(writer->WriteScalar(full_name(kUninitialized), ""));
      return OkStatus();
    }

    Status RestoreInternal(IteratorContext* ctx,
                           IteratorStateReader* reader) override {
      mutex_lock l(mu_);
      if (reader->Contains(full_name(kUninitialized))) {
        input_impl_.reset();
        first_call_ = true;
      } else {
        TF_RETURN_IF_ERROR(
            dataset()->input_->MakeIterator(ctx, this, prefix(), &input_impl_));
        TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
        first_call_ = false;
      }
      return OkStatus();
    }

   private:
    mutex mu_;
    std::unique_ptr<IteratorBase> input_impl_ TF_GUARDED_BY(mu_);
    bool first_call_ TF_GUARDED_BY(mu_);
  };

  const int64_t count_;
  const DatasetBase* const input_;
};

RepeatDatasetOp::RepeatDatasetOp(OpKernelConstruction* ctx)
    : UnaryDatasetOpKernel(ctx) {}

void RepeatDatasetOp::MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                                  DatasetBase** output) {
  // Create a new RepeatDatasetOp::Dataset, insert it in the step-local
  // container, and return it as the output.
  int64_t count;
  OP_REQUIRES_OK(ctx, ParseScalarArgument<int64_t>(ctx, kCount, &count));
  *output = new Dataset(ctx, count, input);
}

namespace {
REGISTER_KERNEL_BUILDER(Name("RepeatDataset").Device(DEVICE_CPU),
                        RepeatDatasetOp);
}  // namespace
}  // namespace data
}  // namespace tensorflow