tensorflow/core/kernels/data/repeat_dataset_op.cc - platform/external/tensorflow - Git at Google

 /* 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 "tensorflow/core/framework/partial_tensor_shape.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/kernels/data/name_utils.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 kKnownRatio = 1;

 class RepeatDatasetOp::Dataset : public DatasetBase {
  public:
   Dataset(OpKernelContext* ctx, int64 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 absl::make_unique<ForeverIterator>(ForeverIterator::Params{
           this, name_utils::IteratorPrefix(kForeverRepeat, prefix)});
     } else if (count_ == 0) {
       return absl::make_unique<EmptyIterator>(EmptyIterator::Params{
           this, name_utils::IteratorPrefix(kEmptyRepeat, prefix)});
     } else {
       return absl::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 Cardinality() const override {
     int64 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;
   }

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

  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 Status::OK();
   }

  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 Status::OK();
     }

    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(IteratorStateWriter* writer) override {
       return Status::OK();
     }
     Status RestoreInternal(IteratorContext* ctx,
                            IteratorStateReader* reader) override {
       return Status::OK();
     }
   };

   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, 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 Status::OK();
       }
       while (i_ < dataset()->count_) {
         TF_RETURN_IF_ERROR(
             input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
         if (!*end_of_sequence) {
           return Status::OK();
         }
         ++i_;
         TF_RETURN_IF_ERROR(
             dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_));
       }
       *end_of_sequence = true;
       input_impl_.reset();
       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=*/kKnownRatio);
     }

     Status SaveInternal(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(writer, input_impl_));
       }
       return Status::OK();
     }

     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 Status::OK();
     }

    private:
     mutex mu_;
     int64 i_ GUARDED_BY(mu_);
     std::unique_ptr<IteratorBase> input_impl_ 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, 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, prefix(), &input_impl_));
         }
         Status s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
         DCHECK(!*end_of_sequence || out_tensors->empty());
         if (first_call_ && *end_of_sequence) {
           // 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 Status::OK();
         }
         first_call_ = false;
         if (!*end_of_sequence) {
           return s;
         } else {
           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(IteratorStateWriter* writer) override {
       mutex_lock l(mu_);
       if (!first_call_)
         TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
       else
         TF_RETURN_IF_ERROR(writer->WriteScalar(full_name(kUninitialized), ""));
       return Status::OK();
     }

     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, prefix(), &input_impl_));
         TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
         first_call_ = false;
       }
       return Status::OK();
     }

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

   const int64 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 count;
   OP_REQUIRES_OK(ctx, ParseScalarArgument<int64>(ctx, kCount, &count));
   *output = new Dataset(ctx, count, input);
 }

 namespace {
 REGISTER_KERNEL_BUILDER(Name("RepeatDataset").Device(DEVICE_CPU),
                         RepeatDatasetOp);
 }  // namespace
 }  // namespace data
 }  // namespace tensorflow
	/* 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 "tensorflow/core/framework/partial_tensor_shape.h"
	#include "tensorflow/core/framework/tensor.h"
	#include "tensorflow/core/kernels/data/name_utils.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 kKnownRatio = 1;

	class RepeatDatasetOp::Dataset : public DatasetBase {
	public:
	Dataset(OpKernelContext* ctx, int64 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 absl::make_unique<ForeverIterator>(ForeverIterator::Params{
	this, name_utils::IteratorPrefix(kForeverRepeat, prefix)});
	} else if (count_ == 0) {
	return absl::make_unique<EmptyIterator>(EmptyIterator::Params{
	this, name_utils::IteratorPrefix(kEmptyRepeat, prefix)});
	} else {
	return absl::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 Cardinality() const override {
	int64 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;
	}

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

	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 Status::OK();
	}

	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 Status::OK();
	}

	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(IteratorStateWriter* writer) override {
	return Status::OK();
	}
	Status RestoreInternal(IteratorContext* ctx,
	IteratorStateReader* reader) override {
	return Status::OK();
	}
	};

	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, 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 Status::OK();
	}
	while (i_ < dataset()->count_) {
	TF_RETURN_IF_ERROR(
	input_impl_->GetNext(ctx, out_tensors, end_of_sequence));
	if (!*end_of_sequence) {
	return Status::OK();
	}
	++i_;
	TF_RETURN_IF_ERROR(
	dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_));
	}
	*end_of_sequence = true;
	input_impl_.reset();
	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=/kKnownRatio);
	}

	Status SaveInternal(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(writer, input_impl_));
	}
	return Status::OK();
	}

	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 Status::OK();
	}

	private:
	mutex mu_;
	int64 i_ GUARDED_BY(mu_);
	std::unique_ptr<IteratorBase> input_impl_ 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, 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, prefix(), &input_impl_));
	}
	Status s = input_impl_->GetNext(ctx, out_tensors, end_of_sequence);
	DCHECK(!*end_of_sequence \|\| out_tensors->empty());
	if (first_call_ && *end_of_sequence) {
	// 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 Status::OK();
	}
	first_call_ = false;
	if (!*end_of_sequence) {
	return s;
	} else {
	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(IteratorStateWriter* writer) override {
	mutex_lock l(mu_);
	if (!first_call_)
	TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
	else
	TF_RETURN_IF_ERROR(writer->WriteScalar(full_name(kUninitialized), ""));
	return Status::OK();
	}

	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, prefix(), &input_impl_));
	TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
	first_call_ = false;
	}
	return Status::OK();
	}

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

	const int64 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 count;
	OP_REQUIRES_OK(ctx, ParseScalarArgument<int64>(ctx, kCount, &count));
	*output = new Dataset(ctx, count, input);
	}

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