EVOLUTION-MANAGER

Edit File: partitioned_function_ops.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_PARTITIONED_FUNCTION_OPS_H_
#define TENSORFLOW_CORE_KERNELS_PARTITIONED_FUNCTION_OPS_H_

#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

class NameAttrList;
class ConfigProto;

// A `PartitionedCallOp` asynchronously executes a function, potentially across
// multiple devices but within a single process. The kernel places and
// partitions a given function's underlying graph, and executes each of the
// partitioned subgraphs as a function.
//
// TODO(akshayka): Support distributed execution.
class PartitionedCallOp : public AsyncOpKernel {
 public:
  explicit PartitionedCallOp(OpKernelConstruction* ctx);

~PartitionedCallOp() override;

void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override;

private:
  Status FillOutputDevices(const FunctionLibraryRuntime& lib,
                           const Device& cpu_device, AttrSlice attrs,
                           FunctionLibraryRuntime::InstantiateOptions* opts);

Status Instantiate(FunctionLibraryRuntime* lib, OpKernelContext* ctx,
                     std::vector<Tensor>* inputs,
                     FunctionLibraryRuntime::Handle* handle);

void RunFunction(FunctionLibraryRuntime::Handle handle,
                   const std::vector<Tensor>& inputs,
                   FunctionLibraryRuntime* lib, OpKernelContext* ctx,
                   DoneCallback done);

// Using unique pointers to avoid including proto headers in kernel headers
  std::unique_ptr<NameAttrList> func_;
  std::unique_ptr<ConfigProto> config_proto_;
  string executor_type_;
  bool shared_rendezvous_;
  mutex mu_;
  // Cache the handle per FLR because this kernel may be instantiated for
  // a stateful op, different invocations of it may use different FLRs.
  // Different device placements of PartitionedCallOp also use
  // different FLRs.
  gtl::FlatMap<FunctionLibraryRuntime*, FunctionLibraryRuntime::Handle> handles_
      TF_GUARDED_BY(mu_);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_PARTITIONED_FUNCTION_OPS_H_