EVOLUTION-MANAGER

Edit File: encapsulate_xla_computations_pass.h

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// Rewrites computations generated by the xla.compile() Python code into
// XlaLaunch nodes.
//
// xla.compile() does two main things:
// a) marks operators that make up an XLA computation with the attribute
//    _xla_compile_id=XYZ, where XYZ is a unique key.
// b) adds XlaClusterOutput nodes to represent outputs of the computation.
//    These nodes are not marked with the _xla_compile_id attribute.

#ifndef TENSORFLOW_COMPILER_JIT_ENCAPSULATE_XLA_COMPUTATIONS_PASS_H_
#define TENSORFLOW_COMPILER_JIT_ENCAPSULATE_XLA_COMPUTATIONS_PASS_H_

#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/env.h"

namespace tensorflow {

// Encapsulates nodes marked with the _xla_compile_id attribute into
// XlaLaunch operators.
class EncapsulateXlaComputationsPass : public GraphOptimizationPass {
 public:
  Status Run(const GraphOptimizationPassOptions& options) override;

// The following methods are public only for unit tests.

// This pass has two stages:
  // a) first, we call EncapsulateSubgraphsPass to encapsulate all nodes
  //    marked with the same _xla_compile_id attribute into functions. These
  //    functions contain the computations to be passed to XlaLaunch. During
  //    encapsulation, we sort the arguments into the order expected by
  //    XlaLaunch.
  static Status Encapsulate(std::unique_ptr<Graph>* graph,
                            FunctionLibraryDefinition* flib_def);

// b) we rewrite the function calls generated in phase (a) into XlaLaunch
  //    operators. We also convert the XlaClusterOutput output nodes of the
  //    function call into the outputs of the XlaLaunch operator.
  static Status BuildXlaLaunchOps(Graph* graph);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_COMPILER_JIT_ENCAPSULATE_XLA_COMPUTATIONS_PASS_H_