text: /* Copyright 2020 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.
  ==============================================================================*/
  #ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_
  #define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_
  
  #include <memory>
  #include <vector>
  
  #include "absl/types/optional.h"
  #include "absl/types/span.h"
  #include "tensorflow/c/eager/abstract_context.h"
  #include "tensorflow/c/eager/immediate_execution_distributed_manager.h"
  #include "tensorflow/c/eager/immediate_execution_operation.h"
  #include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
  #include "tensorflow/c/tensor_interface.h"
  #include "tensorflow/core/framework/function.h"
  #include "tensorflow/core/framework/function.pb.h"
  #include "tensorflow/core/framework/numeric_types.h"
  #include "tensorflow/core/framework/tensor.h"
  #include "tensorflow/core/framework/types.pb.h"
  #include "tensorflow/core/platform/platform.h"
  #include "tensorflow/core/platform/status.h"
  #include "tensorflow/core/platform/tstring.h"
  #include "tensorflow/core/protobuf/config.pb.h"
  #include "tensorflow/core/util/device_name_utils.h"
  
  namespace tensorflow {
  class EagerExecutor;
  class EagerContext;
  class CustomDevice;
  class CustomDeviceOpHandler;
  class Device;
  
  // LINT.IfChange
  // Note: Keep in sync with exported copy of enum in eager/c_api.h.
  enum ContextDevicePlacementPolicy {
    // Running operations with input tensors on the wrong device will fail.
    DEVICE_PLACEMENT_EXPLICIT = 0,
    // Copy the tensor to the right device but log a warning.
    DEVICE_PLACEMENT_WARN = 1,
    // Silently copy the tensor, which has a performance cost since the operation
    // will be blocked till the copy completes. This is the default policy.
    DEVICE_PLACEMENT_SILENT = 2,
    // Placement policy which silently copies int32 tensors but not other dtypes.
    DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
  };
  // LINT.ThenChange(//tensorflow/c/eager/c_api.h)
  
  // Abstract interface to a context.
  //
  // A context is responsible for creating key objects such as Tensors,
  // TensorHandles & Operations.
  class ImmediateExecutionContext : public AbstractContext {
   public:
    // Optimized scalar creation functions
    virtual AbstractTensorInterface* CreateInt64Scalar(int64 value) = 0;
    virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;
    virtual AbstractTensorInterface* CreateInt32Scalar(int32 value) = 0;
    virtual AbstractTensorInterface* CreateFloatScalar(float value) = 0;
    virtual AbstractTensorInterface* CreateDoubleScalar(double value) = 0;
    virtual AbstractTensorInterface* CreateHalfScalar(Eigen::half value) = 0;
    virtual AbstractTensorInterface* CreateStringScalar(tstring value) = 0;
    virtual AbstractTensorInterface* CreateComplex128Scalar(complex128 value) = 0;
    virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;
  
    // Tensor creation functions
    virtual AbstractTensorInterface* CreateTensor(
        DataType dtype, absl::Span<const int64> dim_sizes) = 0;
  
    typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);
  
    // Create a tensor instance from the given data buffer and description.
    // `memory_releaser` will be called on destruction, and it's responsible for
    // cleaning up the underlying buffer.
    virtual AbstractTensorInterface* CreateTensor(
        DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
        MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;
  
    // Create a handle to wrap and manage a Tensor
    virtual ImmediateExecutionTensorHandle* CreateLocalHandle(
        AbstractTensorInterface* t) = 0;
    // Copy the handle to another device.
    virtual ImmediateExecutionTensorHandle* CopyTensorHandleToDevice(
        ImmediateExecutionTensorHandle* handle, const char* device_name,
        Status* status) = 0;
  
    // Create an operation to perform op execution
    ImmediateExecutionOperation* CreateOperation() override = 0;
  
    // Returns whether the runtime is backed by TFRT or the legacy TF Eager
    // Runtime. This is necessary to decouple runtime-dependent
    // code that is layered on top of the runtime.
    virtual bool UsesTFRT() = 0;
  
    // List attributes of available devices
    virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;
  
    // Add logical CPU devices.
    virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0;
  
    // Block until all pending nodes are finished.
    virtual Status AsyncWait() = 0;
  
    // Add a function (serialized FunctionDef protocol buffer) so that it can
    // be executed as an op. Return error if the function with the same name
    // already exists.
    virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;
  
    // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under
    // the key of the function definition name (to be retrieved during function
    // instantiation).
    virtual Status AddFunctionDefWithStackTraces(
        const FunctionDef& fdef, const StackTracesMap& stack_traces) = 0;
  
    // Find and return a added function by its name.
    virtual const FunctionDef* FindFunctionDef(const string& name) const = 0;
  
    // Return the ParsedName of Host CPU device.
    virtual const DeviceNameUtils::ParsedName& HostCPUParsedName() const = 0;
    virtual const string& HostCPUName() const = 0;
  
    // Configure soft device placement policy.
    virtual void SetAllowSoftPlacement(bool enable) = 0;
  
    // Configure device placement policy logging.
    virtual void SetLogDevicePlacement(bool enable) = 0;
  
    // Sets the device placement policy for the current thread.
    virtual void SetThreadLocalDevicePlacementPolicy(
        ContextDevicePlacementPolicy policy) = 0;
    // Returns the device placement policy for the current thread.
    virtual ContextDevicePlacementPolicy GetDevicePlacementPolicy() const = 0;
  
    // Configure graph collection in RunMetadata.
    virtual void SetShouldStoreGraphs(bool value) = 0;
  
    // Return the collected RunMetadata. This method will transfer the ownership
    // to the caller.
    virtual std::unique_ptr<RunMetadata> ExportRunMetadata() = 0;
  
    // For LLVM style RTTI.
    static bool classof(const AbstractContext* ptr) {
      return ptr->getKind() == kEager || ptr->getKind() == kTfrt;
    }
  
    //===--------------------------------------------------------------------===//
    // Experimental Custom Device.
    //===--------------------------------------------------------------------===//
    virtual CustomDeviceOpHandler& GetCustomDeviceOpHandler() = 0;
  
    // Register a custom device. It will return error is the device name is
    // already registered.
    // TODO(tfrt-devs): Remove this method. Let caller register it directly into
    // CustomDeviceOpHandler.
    virtual Status RegisterCustomDevice(const string& name,
                                        std::unique_ptr<CustomDevice> device) = 0;
  
    // Return FunctionLibraryDefinition. Transformations need to use it to use it
    // to invoke MLIR compiler passes.
    virtual FunctionLibraryDefinition* FuncLibDef() = 0;
  
    // When tensor transfer across functions/eager executions using send/recv ops
    // are required, `reuse_rendezvous_for_functions_` can be set to true so that
    // function executions and eager executions use the same rendezvous instance,
    // instead of creating new instance per function calls.
    virtual void SetReuseRendezvousForFunctions(
        bool reuse_rendezvous_for_functions) = 0;
  
    //===--------------------------------------------------------------------===//
    // Following are features in current TF Eager Runtime.
    // TODO(tfrt-devs): Figure out a way to deprecate following features after
    // migrated to TFRT.
    //===--------------------------------------------------------------------===//
    // Clear pending nodes in thread executors and kernel caches.
    virtual void ClearCachesAndThreadExecutors() = 0;
  
    // Initialize the step resource container for a training step. This is used
    // in current TF runtime. For tfrt, it is used by fallback op handler.
    virtual void StartStep() = 0;
    // Destroy the step resource container for a training step.
    virtual void EndStep() = 0;
  
    // Return the Eager Executor for current thread. Please note that Eager
    // Executor is only used in current TF but not in TFRT.
    virtual EagerExecutor& Executor() = 0;
    // Update the Eager Executor for current thread.
    virtual void SetExecutorForThread(EagerExecutor* executor) = 0;
  
    // Return a list of local tensorflow::Device*.
    // TODO(tfrt-devs): We shouldn't expose legacy device in this API.
    virtual std::vector<tensorflow::Device*> ListLocalTfDevices() = 0;
  
    //===--------------------------------------------------------------------===//
    // Following are helper functions to assist integrating TFRT with current
    // TF eager runtime.
    // TODO(b/172877902): These helper functions are currently used to support
    // PyFuncOp on TFRT, and might be useful for ops that directly use low
    // level TF APIs. Remove/replace the following functions when TFRT native
    // ops are implemented.
    //===--------------------------------------------------------------------===//
    // Create an abstract tensor handle from tensorflow::Tensor.
    virtual ImmediateExecutionTensorHandle* CreateLocalHandleFromTFTensor(
        tensorflow::Tensor& t, const char* d_name) = 0;
  
    // Convert a TFRT TensorHandle to tensorflow::TensorHandle.
    virtual ImmediateExecutionTensorHandle* TFTensorHandleFromInterface(
        ImmediateExecutionTensorHandle* handle) = 0;
  
    virtual std::vector<std::string> GetLoggedOpsTestonly() { return {}; }
  
    // Get a list of the names of functions that have been registered.
    virtual std::vector<string> ListFunctionNames() = 0;
  
    //===--------------------------------------------------------------------===//
    // Distributed runtime related functions.
    //===--------------------------------------------------------------------===//
  #if !defined(IS_MOBILE_PLATFORM)
    // Set a distributed manager that helps set up, update, and check liveness
    // of member tasks in the cluster.
    virtual void SetDistributedManager(
        std::unique_ptr<ImmediateExecutionDistributedManager> distributed) = 0;
  
    virtual ImmediateExecutionDistributedManager* GetDistributedManager() = 0;
  #endif  // !IS_MOBILE_PLATFORM
  
   protected:
    explicit ImmediateExecutionContext(AbstractContextKind kind)
        : AbstractContext(kind) {}
    ~ImmediateExecutionContext() override {}
  };
  
  namespace internal {
  struct ImmediateExecutionContextDeleter {
    void operator()(ImmediateExecutionContext* p) const {
      if (p != nullptr) {
        p->Release();
      }
    }
  };
  }  // namespace internal
  
  using ImmediateContextPtr =
      std::unique_ptr<ImmediateExecutionContext,
                      internal::ImmediateExecutionContextDeleter>;
  
  }  // namespace tensorflow
  
  #endif  // TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_
  