path: root/tensorflow/core/kernels/data/optimize_dataset_op.cc

/* Copyright 2018 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 <map>

#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/graph_runner.h"
#include "tensorflow/core/common_runtime/process_function_library_runtime.h"
#include "tensorflow/core/framework/device_base.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/graph_constructor.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/grappler/clusters/virtual_cluster.h"
#include "tensorflow/core/grappler/graph_view.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/grappler_item_builder.h"
#include "tensorflow/core/grappler/optimizers/meta_optimizer.h"
#include "tensorflow/core/kernels/data/dataset.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/rewriter_config.pb.h"

namespace tensorflow {
namespace {

// See documentation in ../ops/dataset_ops.cc for a high-level
// description of the following op.
class OptimizeDatasetOp : public UnaryDatasetOpKernel {
 public:
  explicit OptimizeDatasetOp(OpKernelConstruction* ctx)
      : UnaryDatasetOpKernel(ctx),
        graph_def_version_(ctx->graph_def_version()) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
  }

 protected:
  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                   DatasetBase** output) override {
    std::vector<string> optimizations;
    OP_REQUIRES_OK(
        ctx, ParseVectorArgument<string>(ctx, "optimizations", &optimizations));
    Dataset* dataset =
        new Dataset(ctx, input, optimizations, output_types_, output_shapes_);
    OP_REQUIRES_OK(ctx, dataset->Optimize(ctx));
    *output = dataset;
  }

 private:
  class Dataset : public DatasetBase {
   public:
    Dataset(OpKernelContext* ctx, const DatasetBase* input,
            const std::vector<string>& optimizations,
            const DataTypeVector& output_types,
            const std::vector<PartialTensorShape>& output_shapes)
        : DatasetBase(DatasetContext(ctx)),
          input_(input),
          optimizations_(optimizations),
          output_types_(output_types),
          output_shapes_(output_shapes) {
      input_->Ref();
    }

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

    std::unique_ptr<IteratorBase> MakeIteratorInternal(
        const string& prefix) const override {
      // We do not add a token for the optimization dataset to the prefix. The
      // prefix is used to identify checkpoint elements and since the
      // optimization dataset is excluded from the checkpoint, adding a token
      // here would result in invalid checkpoint identifiers.
      return std::unique_ptr<IteratorBase>(new Iterator({this, prefix}));
    }

    Status Optimize(OpKernelContext* ctx) {
      GraphDefBuilder b;
      DatasetGraphDefBuilder db(&b);
      Node* input_node = nullptr;
      SerializationContext::Params params;
      std::vector<std::pair<string, Tensor>> input_list;
      params.allow_stateful_functions = true;
      params.flib_def = ctx->function_library()->GetFunctionLibraryDefinition();
      params.input_list = &input_list;
      SerializationContext serialization_ctx(params);
      TF_RETURN_IF_ERROR(
          db.AddInputDataset(&serialization_ctx, input_, &input_node));
      string output_node = input_node->name();

      GraphDef graph_def;
      TF_RETURN_IF_ERROR(b.ToGraphDef(&graph_def));
      VLOG(3) << "Before optimization: " << graph_def.DebugString();

      TF_RETURN_IF_ERROR(ApplyOptimizations(ctx, &graph_def, &output_node));
      VLOG(3) << "After optimization: " << graph_def.DebugString();

      // Instantiate the optimized input pipeline by running the optimized graph
      // using the optimized function library.
      TF_RETURN_IF_ERROR(
          ctx->function_library()->Clone(&flib_def_, &pflr_, &lib_));
      TF_RETURN_IF_ERROR(flib_def_->AddLibrary(graph_def.library()));

      Graph graph(OpRegistry::Global());
      TF_RETURN_IF_ERROR(ImportGraphDef({}, graph_def, &graph, nullptr));
      std::vector<Tensor> outputs;
      GraphRunner graph_runner(ctx->function_library()->device());

      TF_RETURN_IF_ERROR(
          graph_runner.Run(&graph, lib_, input_list, {output_node}, &outputs));
      TF_RETURN_IF_ERROR(
          GetDatasetFromVariantTensor(outputs[0], &optimized_input_));
      optimized_input_->Ref();
      return Status::OK();
    }

    const DataTypeVector& output_dtypes() const override {
      return output_types_;
    }
    const std::vector<PartialTensorShape>& output_shapes() const override {
      return output_shapes_;
    }

    string DebugString() const override { return "OptimizeDatasetOp::Dataset"; }

   protected:
    Status AsGraphDefInternal(SerializationContext* ctx,
                              DatasetGraphDefBuilder* b,
                              Node** output) const override {
      // We only serialize the optimized dataset to avoid re-running
      // optimizations when the input pipeline is restored from a checkpoint.
      TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, optimized_input_, output));
      return Status::OK();
    }

   private:
    class Iterator : public DatasetIterator<Dataset> {
     public:
      explicit Iterator(const Params& params)
          : DatasetIterator<Dataset>(params) {}

      Status Initialize(IteratorContext* ctx) override {
        IteratorContext::Params params;
        params.env = ctx->env();
        params.runner = *(ctx->runner());
        params.stats_aggregator_getter = ctx->stats_aggregator_getter();
        params.lib = dataset()->lib_;
        params.allocator_getter = ctx->allocator_getter();
        return dataset()->optimized_input_->MakeIterator(
            IteratorContext(params), prefix(), &input_impl_);
      }

      Status GetNextInternal(IteratorContext* ctx,
                             std::vector<Tensor>* out_tensors,
                             bool* end_of_sequence) override {
        IteratorContext::Params params;
        params.env = ctx->env();
        params.runner = *(ctx->runner());
        params.stats_aggregator_getter = ctx->stats_aggregator_getter();
        params.lib = dataset()->lib_;
        params.allocator_getter = ctx->allocator_getter();
        IteratorContext iter_ctx(params);
        return input_impl_->GetNext(&iter_ctx, out_tensors, end_of_sequence);
      }

     protected:
      Status SaveInternal(IteratorStateWriter* writer) override {
        TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
        return Status::OK();
      }

      Status RestoreInternal(IteratorContext* ctx,
                             IteratorStateReader* reader) override {
        TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
        return Status::OK();
      }

     private:
      std::unique_ptr<IteratorBase> input_impl_;
    };

    Status ApplyOptimizations(OpKernelContext* ctx, GraphDef* graph_def,
                              string* output_node) {
      // Add a fake sink node to allow rewriting the actual sink node.
      NodeDef* node = graph_def->mutable_node()->Add();
      node->set_name("FakeSink");
      node->set_op("SinkDataset");
      node->add_input(*output_node);

      // Create metagraph.
      MetaGraphDef meta_graph_def;
      (*meta_graph_def.mutable_graph_def()) = *graph_def;

      // Grappler determines fetch ops from collection 'train_op'.
      CollectionDef collection_def;
      auto node_list = collection_def.mutable_node_list();
      node_list->add_value("FakeSink");
      (*meta_graph_def.mutable_collection_def())["train_op"] = collection_def;

      // Create Grappler item.
      tensorflow::RewriterConfig rewriter_config;
      for (const string& optimization : optimizations_) {
        rewriter_config.add_optimizers(optimization);
      }
      // If no optimizations were specified, supply a non-existent
      // optimization to prevent Grappler from applying the default set of
      // optimizations as some of them do not work out of the box at the
      // moment (e.g. because we have no cost model for dataset ops).
      if (optimizations_.empty()) {
        rewriter_config.add_optimizers("non-existent");
      }
      tensorflow::grappler::ItemConfig item_config;
      item_config.apply_optimizations = true;
      std::unique_ptr<tensorflow::grappler::GrapplerItem> grappler_item =
          tensorflow::grappler::GrapplerItemFromMetaGraphDef(
              "graph", meta_graph_def, item_config);
      std::unordered_map<string, tensorflow::DeviceProperties> device_map;
      tensorflow::grappler::VirtualCluster cluster(device_map);

      // Run optimizer.
      if (VLOG_IS_ON(2)) {
        LOG(INFO) << "Performing the following optimizations:";
        for (const string& optimization : optimizations_) {
          LOG(INFO) << "  " << optimization;
        }
      }
      TF_RETURN_IF_ERROR(tensorflow::grappler::RunMetaOptimizer(
          *grappler_item, rewriter_config, ctx->device(), &cluster, graph_def));

      // Set `output_node` to the input of the fake sink node.
      {
        grappler::GraphView graph(graph_def);
        grappler::GraphView::InputPort input_port =
            graph.GetInputPort("FakeSink", 0);
        *output_node = graph.GetRegularFanin(input_port).node->name();
      }

      return Status::OK();
    }

    DatasetBase* optimized_input_;
    FunctionLibraryRuntime* lib_ = nullptr;
    std::unique_ptr<ProcessFunctionLibraryRuntime> pflr_ = nullptr;
    std::unique_ptr<FunctionLibraryDefinition> flib_def_ = nullptr;
    const DatasetBase* input_;
    const std::vector<string> optimizations_;
    const DataTypeVector output_types_;
    const std::vector<PartialTensorShape> output_shapes_;
  };

  const int graph_def_version_;
  DataTypeVector output_types_;
  std::vector<PartialTensorShape> output_shapes_;
};

REGISTER_KERNEL_BUILDER(Name("OptimizeDataset").Device(DEVICE_CPU),
                        OptimizeDatasetOp);

}  // namespace
}  // namespace tensorflow