Avoid pointer indirection in handle, context & op

Since the struct lifetime is bound to the wrapped pointer this is fine.

PiperOrigin-RevId: 308941521
Change-Id: I0604fff4fcba6a03cc4a2242ab9f182fbfbf8bae

tensorflow/c/BUILD

@@ -334,6 +334,9 @@ tf_cuda_library(
         ":checkpoint_reader",
         "//tensorflow/c/eager:c_api",
         "//tensorflow/c/eager:c_api_internal",
+        "//tensorflow/c/eager:tfe_context_internal",
+        "//tensorflow/c/eager:tfe_op_internal",
+        "//tensorflow/c/eager:tfe_tensorhandle_internal",
         "//tensorflow/compiler/jit:flags",
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
@@ -729,3 +732,11 @@ tf_cuda_library(
     ],
     alwayslink = 1,
 )
+
+cc_library(
+    name = "conversion_macros",
+    hdrs = [
+        "conversion_macros.h",
+    ],
+    visibility = ["//tensorflow:__subpackages__"],
+)

tensorflow/c/c_api_experimental.cc

@@ -21,6 +21,9 @@ limitations under the License.
 #include "tensorflow/c/checkpoint_reader.h"
 #include "tensorflow/c/eager/c_api.h"
 #include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/compiler/jit/flags.h"
 #include "tensorflow/core/common_runtime/eager/attr_builder.h"
 #include "tensorflow/core/common_runtime/eager/context.h"
@@ -686,8 +689,7 @@ TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
   std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);
 
   status->status = tensorflow::Status::OK();
-  return new TFE_TensorHandle{
+  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor));
-      tensorflow::TensorHandle::CreateLocalHandle(tensor)};
 }
 
 namespace {
@@ -708,7 +710,7 @@ tensorflow::Status EnableCollectiveOps(const tensorflow::ServerDef& server_def,
 
   // New server created for new server_def. Unused if updating server_def.
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   tensorflow::GrpcServer* grpc_server =
       dynamic_cast<tensorflow::GrpcServer*>(context->GetServer());
   if (grpc_server == nullptr) {
@@ -822,14 +824,13 @@ void TFE_InferShapes(TFE_Op* tfe_op, TF_ShapeAndTypeList* input_shapes,
 
   const int num_inputs = input_shapes->num_items;
   NodeDef node_def;
-  node_def.set_name(tfe_op->operation->Name());
+  tensorflow::AbstractOperationInterface* op = tensorflow::unwrap(tfe_op);
-  node_def.set_op(tfe_op->operation->Name());
+  node_def.set_name(op->Name());
+  node_def.set_op(op->Name());
   for (int i = 0; i < num_inputs; ++i) {
     node_def.add_input("dummy_input");
   }
-  OperationFromInterface(tfe_op->operation)
+  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());
-      ->Attrs()
-      .FillAttrValueMap(node_def.mutable_attr());
 
   const tensorflow::OpRegistrationData* op_reg_data;
   status->status =

tensorflow/c/conversion_macros.h

@@ -0,0 +1,30 @@
+/* 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_CONVERSION_MACROS_H_
+#define TENSORFLOW_C_CONVERSION_MACROS_H_
+
+#define DEFINE_CONVERSION_FUNCTIONS(cpp_impl, wrapper) \
+  inline cpp_impl *unwrap(wrapper *w) {                \
+    return reinterpret_cast<cpp_impl *>(w);            \
+  }                                                    \
+                                                       \
+  inline const cpp_impl *unwrap(const wrapper *w) {    \
+    return reinterpret_cast<const cpp_impl *>(w);      \
+  }                                                    \
+                                                       \
+  inline wrapper *wrap(cpp_impl *i) { return reinterpret_cast<wrapper *>(i); }
+
+#endif  // TENSORFLOW_C_CONVERSION_MACROS_H_

tensorflow/c/eager/BUILD

@@ -50,7 +50,6 @@ tf_cuda_library(
             ":tfe_tensor_debug_info_internal",
             ":tfe_tensorhandle_internal",
             "@com_google_absl//absl/algorithm:container",
-            "@com_google_absl//absl/container:fixed_array",
             "@com_google_absl//absl/types:span",
             "@com_google_absl//absl/types:variant",
             "//tensorflow/c:c_api",
@@ -110,13 +109,10 @@ filegroup(
         "operation_interface.h",
         "tensor_handle_interface.h",
         "tfe_cancellation_manager_internal.h",
-        "tfe_context_internal.h",
         "tfe_executor_internal.h",
         "tfe_monitoring_internal.h",
         "tfe_op_attrs_internal.h",
-        "tfe_op_internal.h",
         "tfe_tensor_debug_info_internal.h",
-        "tfe_tensorhandle_internal.h",
     ],
     visibility = [
         "//tensorflow/core:__pkg__",
@@ -205,6 +201,7 @@ cc_library(
     ],
     deps = [
         ":context_interface",
+        "//tensorflow/c:conversion_macros",
     ],
 )
 
@@ -249,8 +246,6 @@ cc_library(
         "//tensorflow:internal",
     ],
     deps = [
-        ":tfe_context_internal",
-        ":tfe_op_internal",
         "//tensorflow/c:tf_status",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/common_runtime/eager:attr_builder",
@@ -265,6 +260,7 @@ cc_library(
     ],
     deps = [
         ":operation_interface",
+        "//tensorflow/c:conversion_macros",
     ],
 )
 
@@ -287,6 +283,7 @@ cc_library(
     ],
     deps = [
         ":tensor_handle_interface",
+        "//tensorflow/c:conversion_macros",
     ],
 )
 
@@ -327,6 +324,8 @@ tf_cuda_cc_test(
         ":c_api_experimental",
         ":c_api_internal",
         ":c_api_test_util",
+        ":tfe_op_internal",
+        ":tfe_tensorhandle_internal",
         "//tensorflow/c:c_test_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
@@ -351,6 +350,7 @@ tf_cuda_cc_test(
         ":c_api_experimental",
         ":c_api_internal",
         ":c_api_test_util",
+        ":tfe_tensorhandle_internal",
         "//tensorflow/c:c_test_util",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
@@ -384,6 +384,9 @@ tf_cuda_library(
         "//conditions:default": [
             ":c_api",
             ":c_api_internal",
+            ":tfe_context_internal",
+            ":tfe_op_internal",
+            ":tfe_tensorhandle_internal",
             "//tensorflow/c:c_api",
             "//tensorflow/c:c_api_internal",
             "//tensorflow/core:core_cpu",
@@ -548,8 +551,9 @@ cc_library(
     deps = [
         ":c_api",
         ":c_api_experimental",
-        ":c_api_internal",
+        ":tfe_tensorhandle_internal",
         "//tensorflow/c:tf_status_helper",
+        "//tensorflow/c:tf_status_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
         "//tensorflow/core:lib",

tensorflow/c/eager/c_api.cc

@@ -26,7 +26,6 @@ limitations under the License.
 // clang-format on
 
 #include "absl/algorithm/container.h"
-#include "absl/container/fixed_array.h"
 #include "absl/memory/memory.h"
 #include "tensorflow/c/c_api.h"
 #include "tensorflow/c/c_api_internal.h"
@@ -34,6 +33,9 @@ limitations under the License.
 #include "tensorflow/c/eager/c_api_internal.h"
 #include "tensorflow/c/eager/operation_interface.h"
 #include "tensorflow/c/eager/tensor_handle_interface.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/c/tf_tensor_internal.h"
 #ifdef PLATFORM_GOOGLE
 #include "tensorflow/c/eager/c_api_tfrt.h"
@@ -298,7 +300,7 @@ tensorflow::Status CreateRemoteContexts(
 
     std::vector<bool> filtered_device_mask;
     tensorflow::EagerContext* context =
-        tensorflow::ContextFromInterface(ctx->context);
+        tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
     context->FilterDevicesForRemoteWorkers(
         remote_worker, base_request.cluster_device_attributes(),
         &filtered_device_mask);
@@ -383,7 +385,7 @@ tensorflow::Status UpdateRemoteContexts(
 
     std::vector<bool> filtered_device_mask;
     tensorflow::EagerContext* context =
-        tensorflow::ContextFromInterface(ctx->context);
+        tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
     context->FilterDevicesForRemoteWorkers(
         remote_worker, base_request.cluster_device_attributes(),
         &filtered_device_mask);
@@ -464,7 +466,7 @@ tensorflow::Status UpdateTFE_ContextWithServerDef(
   // New server created for new server_def. Unused if updating server_def.
   std::unique_ptr<tensorflow::ServerInterface> new_server;
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   tensorflow::GrpcServer* grpc_server;
   if (reset_context) {
     LOG_AND_RETURN_IF_ERROR(tensorflow::NewServer(server_def, &new_server));
@@ -684,7 +686,7 @@ TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) {
   if (opts->use_tfrt) {
 #ifdef PLATFORM_GOOGLE
     status->status = tensorflow::Status::OK();
-    return new TFE_Context{new tfrt::ContextInterface()};
+    return tensorflow::wrap(new tfrt::ContextInterface());
 #else
     status->status = tensorflow::errors::Unimplemented("TFRT is not supported");
     return nullptr;
@@ -701,14 +703,14 @@ TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) {
   tensorflow::Rendezvous* r =
       new tensorflow::IntraProcessRendezvous(device_mgr.get());
 
-  return new TFE_Context{new tensorflow::EagerContext(
+  return tensorflow::wrap(new tensorflow::EagerContext(
       opts->session_options.options,
       static_cast<tensorflow::ContextDevicePlacementPolicy>(
           opts->device_placement_policy),
       static_cast<tensorflow::ContextMirroringPolicy>(opts->mirroring_policy),
       opts->async, opts->lazy_remote_inputs_copy, device_mgr.release(),
       /*device_mgr_owned*/ true, r,
-      tensorflow::GetDefaultCustomKernelCreator())};
+      tensorflow::GetDefaultCustomKernelCreator()));
 }
 
 TFE_Context* TFE_NewContextFromSession(const TFE_ContextOptions* opts,
@@ -719,14 +721,14 @@ TFE_Context* TFE_NewContextFromSession(const TFE_ContextOptions* opts,
   tensorflow::Rendezvous* r =
       new tensorflow::IntraProcessRendezvous(device_mgr);
 
-  return new TFE_Context{new tensorflow::EagerContext(
+  return tensorflow::wrap(new tensorflow::EagerContext(
       opts->session_options.options,
       static_cast<tensorflow::ContextDevicePlacementPolicy>(
           opts->device_placement_policy),
       static_cast<tensorflow::ContextMirroringPolicy>(opts->mirroring_policy),
       opts->async, opts->lazy_remote_inputs_copy, device_mgr,
       /*device_mgr_owned*/ false, r,
-      tensorflow::GetDefaultCustomKernelCreator())};
+      tensorflow::GetDefaultCustomKernelCreator()));
 }
 
 void TFE_DeleteContext(TFE_Context* ctx) {
@@ -734,22 +736,19 @@ void TFE_DeleteContext(TFE_Context* ctx) {
     return;
   }
 
-  // context->RefCountIsOne() should be true here.
+  // ctx->RefCountIsOne() should be true here.
-  // TODO(iga): Remove EagerContext refcounting.
+  tensorflow::unwrap(ctx)->Release();
-  ctx->context->Release();
-
-  delete ctx;
 }
 
 TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) {
   TF_DeviceList* l = new TF_DeviceList;
-  ctx->context->ListDevices(&l->response);
+  tensorflow::unwrap(ctx)->ListDevices(&l->response);
   return l;
 }
 
 void TFE_ContextClearCaches(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->ClearCachesAndThreadExecutors();
 }
 
@@ -772,7 +771,7 @@ TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
   if (server_def.has_cluster_device_filters()) {
     const auto& cdf = server_def.cluster_device_filters();
     for (const auto& jdf : cdf.jobs()) {
-      const string& remote_prefix = "/job:" + jdf.name() + "/task:";
+      const string remote_prefix = "/job:" + jdf.name() + "/task:";
       for (const auto& tdf : jdf.tasks()) {
         const int32_t task_index = tdf.first;
         std::vector<string> device_filters(tdf.second.device_filters_size());
@@ -781,7 +780,7 @@ TF_CAPI_EXPORT extern void TFE_ContextSetServerDef(TFE_Context* ctx,
         }
         const string remote_worker = remote_prefix + std::to_string(task_index);
         tensorflow::EagerContext* context =
-            tensorflow::ContextFromInterface(ctx->context);
+            tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
         status->status =
             context->SetRemoteDeviceFilters(remote_worker, device_filters);
       }
@@ -803,7 +802,7 @@ TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDef(TFE_Context* ctx,
 #else   // !defined(IS_MOBILE_PLATFORM)
   tensorflow::ServerDef server_def;
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   if (!server_def.ParseFromArray(proto, proto_len)) {
     status->status = tensorflow::errors::InvalidArgument(
         "Invalid tensorflow.ServerDef protocol buffer");
@@ -833,7 +832,7 @@ TF_CAPI_EXPORT extern bool TFE_ContextCheckAlive(TFE_Context* ctx,
   return false;
 #else   // !defined(IS_MOBILE_PLATFORM)
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   tensorflow::GrpcServer* grpc_server =
       static_cast<tensorflow::GrpcServer*>(context->GetServer());
 
@@ -889,7 +888,7 @@ TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,
   status->status = tensorflow::Status::OK();
 #else   // !defined(IS_MOBILE_PLATFORM)
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->SyncExecutors();
 #endif  // !IS_MOBILE_PLATFORM
 }
@@ -897,7 +896,7 @@ TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,
 void TFE_ContextSetThreadLocalDevicePlacementPolicy(
     TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetThreadLocalDevicePlacementPolicy(
       static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
 }
@@ -908,7 +907,7 @@ void TFE_ContextSetThreadLocalDevicePlacementPolicy(
 extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(
     TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   return static_cast<TFE_ContextDevicePlacementPolicy>(
       context->GetDevicePlacementPolicy());
 }
@@ -918,8 +917,7 @@ TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) {
   status->status = tensorflow::TF_TensorToTensor(t, &tensor);
   if (!status->status.ok()) return nullptr;
 
-  return new TFE_TensorHandle{
+  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(tensor));
-      tensorflow::TensorHandle::CreateLocalHandle(tensor)};
 }
 
 void TFE_DeleteTensorHandle(TFE_TensorHandle* h) {
@@ -927,84 +925,84 @@ void TFE_DeleteTensorHandle(TFE_TensorHandle* h) {
 
   tensorflow::profiler::TraceMe activity(
       "TFE_DeleteTensorHandle", tensorflow::profiler::TraceMeLevel::kInfo);
-  if (h->handle) {
+  if (h) {
-    h->handle->Release();
+    tensorflow::unwrap(h)->Release();
   }
-  delete h;
 }
 
 TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) {
-  return static_cast<TF_DataType>(h->handle->DataType());
+  return static_cast<TF_DataType>(tensorflow::unwrap(h)->DataType());
 }
 
 int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return -1;
   }
 
   int num_dims = -1;
-  status->status = h->handle->NumDims(&num_dims);
+  status->status = tensorflow::unwrap(h)->NumDims(&num_dims);
   return num_dims;
 }
 
 int64_t TFE_TensorHandleNumElements(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return -1;
   }
 
   int64 num_elements = -1;
-  status->status = h->handle->NumElements(&num_elements);
+  status->status = tensorflow::unwrap(h)->NumElements(&num_elements);
   return num_elements;
 }
 
 int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                             TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return -1;
   }
 
   int64 dim = -1;
-  status->status = h->handle->Dim(dim_index, &dim);
+  status->status = tensorflow::unwrap(h)->Dim(dim_index, &dim);
   return dim;
 }
 
 const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
-  return h->handle->DeviceName(&status->status);
+  return tensorflow::unwrap(h)->DeviceName(&status->status);
 }
 
 const char* TFE_TensorHandleBackingDeviceName(TFE_TensorHandle* h,
                                               TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
-  return h->handle->BackingDeviceName(&status->status);
+  return tensorflow::unwrap(h)->BackingDeviceName(&status->status);
 }
 
 TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopySharingTensor(
     TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
 
-  return new TFE_TensorHandle{h->handle->Copy()};
+  return tensorflow::wrap(tensorflow::unwrap(h)->Copy());
 }
 
 TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
 
-  tensorflow::AbstractTensorInterface* t = h->handle->Resolve(&status->status);
+  tensorflow::AbstractTensorInterface* t =
+      tensorflow::unwrap(h)->Resolve(&status->status);
   if (t == nullptr) {
     return nullptr;
   }
@@ -1013,12 +1011,12 @@ TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h, TF_Status* status) {
 }
 
 void* TFE_TensorHandleDevicePointer(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
   tensorflow::TensorHandle* handle =
-      tensorflow::TensorHandleFromInterface(h->handle);
+      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
   if (VariantDeviceIsCustom(handle->device())) {
     const tensorflow::Tensor* t;
     status->status = handle->Tensor(&t);
@@ -1054,7 +1052,7 @@ TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
     void* deallocator_arg, TF_Status* status) {
   tensorflow::Device* device = nullptr;
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->FindDeviceFromName(device_name, &device);
   tensorflow::CustomDevice* custom_device = nullptr;
   if (!status->status.ok()) {
@@ -1080,11 +1078,11 @@ TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
                        tensorflow::TensorShape(dimvec), buf);
   buf->Unref();
   if (custom_device == nullptr) {
-    return new TFE_TensorHandle{tensorflow::TensorHandle::CreateLocalHandle(
+    return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(
-        std::move(t), device, device, context)};
+        std::move(t), device, device, context));
   } else {
-    return new TFE_TensorHandle{tensorflow::TensorHandle::CreateLocalHandle(
+    return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(
-        std::move(t), custom_device, context)};
+        std::move(t), custom_device, context));
   }
 }
 
@@ -1093,12 +1091,12 @@ TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(
 // bytes of the memory pointed to by the device pointer returned above.
 size_t TFE_TensorHandleDeviceMemorySize(TFE_TensorHandle* h,
                                         TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return 0;
   }
   tensorflow::TensorHandle* handle =
-      tensorflow::TensorHandleFromInterface(h->handle);
+      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
   if (handle->Type() != tensorflow::TensorHandle::LOCAL) {
     status->status = tensorflow::errors::InvalidArgument(
         "TFE_TensorHandleDeviceMemorySize may not be called on a ",
@@ -1115,12 +1113,14 @@ size_t TFE_TensorHandleDeviceMemorySize(TFE_TensorHandle* h,
 
 TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name,
                   TF_Status* status) {
-  std::unique_ptr<TFE_Op> new_op(new TFE_Op{ctx->context->CreateOperation()});
+  tensorflow::AbstractOperationInterface* new_op =
-  status->status = new_op->operation->Reset(op_or_function_name, nullptr);
+      tensorflow::unwrap(ctx)->CreateOperation();
+  status->status = new_op->Reset(op_or_function_name, nullptr);
   if (!status->status.ok()) {
-    new_op.reset();
+    new_op->Release();
+    new_op = nullptr;
   }
-  return new_op.release();
+  return tensorflow::wrap(new_op);
 }
 
 void TFE_DeleteOp(TFE_Op* op) {
@@ -1128,24 +1128,20 @@ void TFE_DeleteOp(TFE_Op* op) {
     return;
   }
 
-  if (op->operation) {
+  tensorflow::unwrap(op)->Release();
-    op->operation->Release();
-  }
-
-  delete op;
 }
 
 void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) {
-  status->status = op->operation->SetDeviceName(device_name);
+  status->status = tensorflow::unwrap(op)->SetDeviceName(device_name);
 }
 
 const char* TFE_OpGetDevice(TFE_Op* op, TF_Status* status) {
-  return op->operation->DeviceName().c_str();
+  return tensorflow::unwrap(op)->DeviceName().c_str();
 }
 
 void TFE_OpSetXLACompilation(TFE_Op* op, unsigned char enable) {
 #ifdef TENSORFLOW_EAGER_USE_XLA
-  tensorflow::Status s = op->operation->SetUseXla(enable);
+  tensorflow::Status s = tensorflow::unwrap(op)->SetUseXla(enable);
   if (!s.ok()) {
     LOG(ERROR) << "Could not enable XLA compilation for op: " << s;
   }
@@ -1156,18 +1152,13 @@ void TFE_OpSetXLACompilation(TFE_Op* op, unsigned char enable) {
 }
 
 void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* input, TF_Status* status) {
-  status->status = op->operation->AddInput(input->handle);
+  status->status = tensorflow::unwrap(op)->AddInput(tensorflow::unwrap(input));
 }
 
 void TFE_OpAddInputList(TFE_Op* op, TFE_TensorHandle** inputs, int num_inputs,
                         TF_Status* status) {
-  absl::FixedArray<tensorflow::AbstractTensorHandleInterface*> handles(
+  status->status = tensorflow::unwrap(op)->AddInputList(
-      num_inputs);
+      {tensorflow::unwrap(inputs), static_cast<size_t>(num_inputs)});
-  for (int i = 0; i < num_inputs; ++i) {
-    handles[i] = inputs[i]->handle;
-  }
-  status->status =
-      op->operation->AddInputList({handles.data(), handles.size()});
 }
 
 TF_AttrType TFE_OpGetAttrType(TFE_Op* op, const char* attr_name,
@@ -1175,8 +1166,8 @@ TF_AttrType TFE_OpGetAttrType(TFE_Op* op, const char* attr_name,
   TF_AttrType ret = TF_ATTR_INT;
   const tensorflow::AttrTypeMap* attr_types_;
   bool is_function;
-  status->status = tensorflow::AttrTypeMapForOp(op->operation->Name().c_str(),
+  status->status = tensorflow::AttrTypeMapForOp(
-                                                &attr_types_, &is_function);
+      tensorflow::unwrap(op)->Name().c_str(), &attr_types_, &is_function);
   if (!status->status.ok()) {
     return ret;
   }
@@ -1202,7 +1193,7 @@ TF_AttrType TFE_OpNameGetAttrType(TFE_Context* ctx,
 
 void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const void* value,
                          size_t length) {
-  auto s = op->operation->SetAttrString(
+  auto s = tensorflow::unwrap(op)->SetAttrString(
       attr_name, static_cast<const char*>(value), length);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
@@ -1210,29 +1201,30 @@ void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name, const void* value,
 }
 
 void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value) {
-  auto s = op->operation->SetAttrInt(attr_name, value);
+  auto s = tensorflow::unwrap(op)->SetAttrInt(attr_name, value);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
 }
 
 void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value) {
-  auto s = op->operation->SetAttrFloat(attr_name, value);
+  auto s = tensorflow::unwrap(op)->SetAttrFloat(attr_name, value);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
 }
 
 void TFE_OpSetAttrBool(TFE_Op* op, const char* attr_name, unsigned char value) {
-  auto s = op->operation->SetAttrBool(attr_name, (value == 0) ? false : true);
+  auto s = tensorflow::unwrap(op)->SetAttrBool(attr_name,
+                                               (value == 0) ? false : true);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
 }
 
 void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name, TF_DataType value) {
-  auto s = op->operation->SetAttrType(attr_name,
+  auto s = tensorflow::unwrap(op)->SetAttrType(
-                                      static_cast<tensorflow::DataType>(value));
+      attr_name, static_cast<tensorflow::DataType>(value));
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1240,12 +1232,14 @@ void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name, TF_DataType value) {
 
 void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name, const int64_t* dims,
                         const int num_dims, TF_Status* out_status) {
-  out_status->status = op->operation->SetAttrShape(attr_name, dims, num_dims);
+  out_status->status =
+      tensorflow::unwrap(op)->SetAttrShape(attr_name, dims, num_dims);
 }
 
 void TFE_OpSetAttrFunction(TFE_Op* op, const char* attr_name,
                            const TFE_Op* value) {
-  auto s = op->operation->SetAttrFunction(attr_name, value->operation);
+  auto s = tensorflow::unwrap(op)->SetAttrFunction(
+      attr_name, tensorflow::unwrap(const_cast<TFE_Op*>(value)));
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1253,7 +1247,7 @@ void TFE_OpSetAttrFunction(TFE_Op* op, const char* attr_name,
 
 void TFE_OpSetAttrFunctionName(TFE_Op* op, const char* attr_name,
                                const char* data, size_t length) {
-  auto s = op->operation->SetAttrFunctionName(attr_name, data, length);
+  auto s = tensorflow::unwrap(op)->SetAttrFunctionName(attr_name, data, length);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1264,14 +1258,14 @@ void TFE_OpSetAttrTensor(TFE_Op* op, const char* attr_name, TF_Tensor* tensor,
   tensorflow::Tensor t;
   status->status = TF_TensorToTensor(tensor, &t);
   tensorflow::TensorInterface interface(t);
-  status->status = op->operation->SetAttrTensor(attr_name, &interface);
+  status->status = tensorflow::unwrap(op)->SetAttrTensor(attr_name, &interface);
 }
 
 void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name,
                              const void* const* values, const size_t* lengths,
                              int num_values) {
-  auto s =
+  auto s = tensorflow::unwrap(op)->SetAttrStringList(attr_name, values, lengths,
-      op->operation->SetAttrStringList(attr_name, values, lengths, num_values);
+                                                     num_values);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1279,7 +1273,8 @@ void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name,
 
 void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name,
                             const float* values, int num_values) {
-  auto s = op->operation->SetAttrFloatList(attr_name, values, num_values);
+  auto s =
+      tensorflow::unwrap(op)->SetAttrFloatList(attr_name, values, num_values);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1287,7 +1282,8 @@ void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name,
 
 void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name,
                           const int64_t* values, int num_values) {
-  auto s = op->operation->SetAttrIntList(attr_name, values, num_values);
+  auto s =
+      tensorflow::unwrap(op)->SetAttrIntList(attr_name, values, num_values);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1295,7 +1291,7 @@ void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name,
 
 void TFE_OpSetAttrTypeList(TFE_Op* op, const char* attr_name,
                            const TF_DataType* values, int num_values) {
-  auto s = op->operation->SetAttrTypeList(
+  auto s = tensorflow::unwrap(op)->SetAttrTypeList(
       attr_name, reinterpret_cast<const tensorflow::DataType*>(values),
       num_values);
   if (!s.ok()) {
@@ -1305,7 +1301,8 @@ void TFE_OpSetAttrTypeList(TFE_Op* op, const char* attr_name,
 
 void TFE_OpSetAttrBoolList(TFE_Op* op, const char* attr_name,
                            const unsigned char* values, int num_values) {
-  auto s = op->operation->SetAttrBoolList(attr_name, values, num_values);
+  auto s =
+      tensorflow::unwrap(op)->SetAttrBoolList(attr_name, values, num_values);
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1314,19 +1311,14 @@ void TFE_OpSetAttrBoolList(TFE_Op* op, const char* attr_name,
 void TFE_OpSetAttrShapeList(TFE_Op* op, const char* attr_name,
                             const int64_t** dims, const int* num_dims,
                             int num_values, TF_Status* out_status) {
-  out_status->status =
+  out_status->status = tensorflow::unwrap(op)->SetAttrShapeList(
-      op->operation->SetAttrShapeList(attr_name, dims, num_dims, num_values);
+      attr_name, dims, num_dims, num_values);
 }
 
 void TFE_OpSetAttrFunctionList(TFE_Op* op, const char* attr_name,
                                const TFE_Op** value, int num_values) {
-  absl::FixedArray<const tensorflow::AbstractOperationInterface*> values(
+  auto s = tensorflow::unwrap(op)->SetAttrFunctionList(
-      num_values);
+      attr_name, {tensorflow::unwrap(value), static_cast<size_t>(num_values)});
-  for (int i = 0; i < num_values; ++i) {
-    values[i] = value[i]->operation;
-  }
-  auto s = op->operation->SetAttrFunctionList(attr_name,
-                                              {values.data(), values.size()});
   if (!s.ok()) {
     LOG(WARNING) << "Unable to set attribute: " << attr_name;
   }
@@ -1341,12 +1333,13 @@ void TFE_OpSetAttrValueProto(const TFE_Op* op, const char* attr_name,
         tensorflow::errors::InvalidArgument("Unparseable AttrValue proto");
     return;
   }
-  if (op == nullptr || op->operation == nullptr) {
+  if (op == nullptr) {
     status->status = tensorflow::errors::InvalidArgument(
         "Got a null or uninitialized `op` argument");
     return;
   }
-  tensorflow::EagerOperation* operation = OperationFromInterface(op->operation);
+  tensorflow::EagerOperation* operation =
+      OperationFromInterface(tensorflow::unwrap(const_cast<TFE_Op*>(op)));
   operation->MutableAttrs()->Set(attr_name, attr_value);
 }
 
@@ -1354,7 +1347,7 @@ TF_CAPI_EXPORT extern int TFE_OpGetInputLength(TFE_Op* op,
                                                const char* input_name,
                                                TF_Status* status) {
   int ret = -1;
-  status->status = op->operation->InputLength(input_name, &ret);
+  status->status = tensorflow::unwrap(op)->InputLength(input_name, &ret);
   return ret;
 }
 
@@ -1362,36 +1355,29 @@ TF_CAPI_EXPORT extern int TFE_OpGetOutputLength(TFE_Op* op,
                                                 const char* output_name,
                                                 TF_Status* status) {
   int ret = -1;
-  status->status = op->operation->OutputLength(output_name, &ret);
+  status->status = tensorflow::unwrap(op)->OutputLength(output_name, &ret);
   return ret;
 }
 
 void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,
                  TF_Status* status) {
-  absl::FixedArray<tensorflow::AbstractTensorHandleInterface*> handles(
+  status->status = tensorflow::unwrap(op)->Execute(
-      *num_retvals);
+      absl::MakeSpan(tensorflow::unwrap(retvals), *num_retvals), num_retvals);
-  status->status = op->operation->Execute(absl::MakeSpan(handles), num_retvals);
-  if (!status->status.ok()) {
-    return;
-  }
-  for (int i = 0; i < *num_retvals; ++i) {
-    retvals[i] = new TFE_TensorHandle{handles[i]};
-  }
 }
 
 TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
                                                TFE_Context* ctx,
                                                const char* device_name,
                                                TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
 
-  auto* result = ctx->context->CopyTensorHandleToDevice(h->handle, device_name,
+  auto* result = tensorflow::unwrap(ctx)->CopyTensorHandleToDevice(
-                                                        &status->status);
+      tensorflow::unwrap(h), device_name, &status->status);
   if (status->status.ok()) {
-    return new TFE_TensorHandle{result};
+    return tensorflow::wrap(result);
   }
   return nullptr;
 }
@@ -1406,39 +1392,39 @@ void TFE_ContextAddFunctionDef(TFE_Context* ctx,
     return;
   }
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->AddFunctionDef(function_def);
 }
 
 void TFE_ContextAddFunction(TFE_Context* ctx, TF_Function* function,
                             TF_Status* status) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->AddFunctionDef(function->fdef);
 }
 
 void TFE_ContextRemoveFunction(TFE_Context* ctx, const char* name,
                                TF_Status* status) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->RemoveFunction(name);
 }
 
 unsigned char TFE_ContextHasFunction(TFE_Context* ctx, const char* name) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   return context->FindFunctionDef(name) != nullptr;
 }
 
 void TFE_ContextEnableRunMetadata(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetShouldStoreGraphs(true);
 }
 
 void TFE_ContextDisableRunMetadata(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetShouldStoreGraphs(false);
 }
 
@@ -1446,13 +1432,13 @@ void TFE_ContextDisableRunMetadata(TFE_Context* ctx) {
 
 TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t,
                                       TF_Status* status) {
-  return new TFE_TensorHandle{tensorflow::TensorHandle::CreateLocalHandle(t)};
+  return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle(t));
 }
 
 void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf,
                                   TF_Status* status) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status = context->Executor().WaitForAllPendingNodes();
   if (!status->status.ok()) return;
   tensorflow::mutex_lock ml(*context->MetadataMu());
@@ -1475,20 +1461,21 @@ TFE_Op* GetFunc(TFE_Context* ctx, const tensorflow::NameAttrList& func,
 
 void TFE_ContextStartStep(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->StartStep();
 }
 
 void TFE_ContextEndStep(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->EndStep();
 }
 
 void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs) {
   tensorflow::AttrValueMap m;
   attrs->attributes->FillAttrValueMap(&m);
-  tensorflow::EagerOperation* operation = OperationFromInterface(op->operation);
+  tensorflow::EagerOperation* operation =
+      OperationFromInterface(tensorflow::unwrap(op));
   tensorflow::AttrBuilder* destination = operation->MutableAttrs();
   for (const auto& attribute : m) {
     destination->Set(attribute.first, attribute.second);
@@ -1576,33 +1563,34 @@ class CustomDeviceAPI : public tensorflow::CustomDevice {
   const string& name() override { return name_; }
 
   tensorflow::Status CopyTensorToDevice(
-      tensorflow::TensorHandle* tensor,
+      tensorflow::TensorHandle* handle,
       tensorflow::TensorHandle** result) override {
-    tensor->Ref();
+    handle->Ref();
-    TFE_TensorHandle tensor_handle{tensor};
     TF_Status status;
-    TFE_TensorHandle* result_handle =
+    TFE_TensorHandle* result_handle = device_.copy_tensor_to_device(
-        device_.copy_tensor_to_device(context_, &tensor_handle, &status, info_);
+        context_, tensorflow::wrap(handle), &status, info_);
-    tensor_handle.handle->Release();
+    handle->Release();
     if (!status.status.ok()) return status.status;
-    *result = tensorflow::TensorHandleFromInterface(result_handle->handle);
+    *result = tensorflow::TensorHandleFromInterface(
+        tensorflow::unwrap(result_handle));
     (*result)->Ref();
     TFE_DeleteTensorHandle(result_handle);
     return status.status;
   }
 
   tensorflow::Status CopyTensorFromDevice(
-      tensorflow::TensorHandle* tensor,
+      tensorflow::TensorHandle* handle,
       const tensorflow::string& target_device_name,
       tensorflow::TensorHandle** result) override {
     TF_Status status;
-    tensor->Ref();
+    handle->Ref();
-    TFE_TensorHandle tensor_handle{tensor};
     TFE_TensorHandle* result_handle = device_.copy_tensor_from_device(
-        context_, &tensor_handle, target_device_name.c_str(), &status, info_);
+        context_, tensorflow::wrap(handle), target_device_name.c_str(), &status,
-    tensor_handle.handle->Release();
+        info_);
+    handle->Release();
     if (!status.status.ok()) return status.status;
-    *result = tensorflow::TensorHandleFromInterface(result_handle->handle);
+    *result = tensorflow::TensorHandleFromInterface(
+        tensorflow::unwrap(result_handle));
     (*result)->Ref();
     TFE_DeleteTensorHandle(result_handle);
     return status.status;
@@ -1615,7 +1603,7 @@ class CustomDeviceAPI : public tensorflow::CustomDevice {
     inputs.reserve(op->Inputs().size());
     for (int i = 0; i < op->Inputs().size(); ++i) {
       op->Inputs()[i]->Ref();
-      inputs.push_back(new TFE_TensorHandle{op->Inputs()[i]});
+      inputs.push_back(tensorflow::wrap(op->Inputs()[i]));
     }
     std::vector<TFE_TensorHandle*> outputs(*num_retvals);
     TF_Status status;
@@ -1624,7 +1612,8 @@ class CustomDeviceAPI : public tensorflow::CustomDevice {
                     &attributes, num_retvals, outputs.data(), &status, info_);
     if (status.status.ok()) {
       for (int i = 0; i < *num_retvals; ++i) {
-        retvals[i] = tensorflow::TensorHandleFromInterface(outputs[i]->handle);
+        retvals[i] = tensorflow::TensorHandleFromInterface(
+            tensorflow::unwrap(outputs[i]));
         retvals[i]->Ref();
         TFE_DeleteTensorHandle(outputs[i]);
       }
@@ -1652,7 +1641,7 @@ void TFE_RegisterCustomDevice(TFE_Context* ctx, TFE_CustomDevice device,
   auto custom_device =
       std::make_unique<CustomDeviceAPI>(ctx, device, device_info, device_name);
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   status->status =
       context->RegisterCustomDevice(device_name, std::move(custom_device));
 }

tensorflow/c/eager/c_api_debug.cc

@@ -57,7 +57,8 @@ extern "C" {
 
 TF_CAPI_EXPORT extern TFE_TensorDebugInfo* TFE_TensorHandleTensorDebugInfo(
     TFE_TensorHandle* h, TF_Status* status) {
-  tensorflow::TensorHandle* handle = TensorHandleFromInterface(h->handle);
+  tensorflow::TensorHandle* handle =
+      TensorHandleFromInterface(tensorflow::unwrap(h));
   const tensorflow::Tensor* tensor;
   status->status = handle->Tensor(&tensor);
   if (!status->status.ok()) {

tensorflow/c/eager/c_api_experimental.cc

@@ -19,6 +19,9 @@ limitations under the License.
 
 #include "tensorflow/c/c_api.h"
 #include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/c/tf_status_helper.h"
 #include "tensorflow/core/common_runtime/device.h"
 #include "tensorflow/core/common_runtime/eager/eager_operation.h"
@@ -34,9 +37,10 @@ using tensorflow::string;
 void TFE_OpReset(TFE_Op* op_to_reset, const char* op_or_function_name,
                  const char* raw_device_name, TF_Status* status) {
   if (op_to_reset) {
-    op_to_reset->operation->Clear();
+    tensorflow::AbstractOperationInterface* op =
-    status->status =
+        tensorflow::unwrap(op_to_reset);
-        op_to_reset->operation->Reset(op_or_function_name, raw_device_name);
+    op->Clear();
+    status->status = op->Reset(op_or_function_name, raw_device_name);
   } else {
     TF_SetStatus(status, TF_INVALID_ARGUMENT,
                  "op_to_reset should not be nullptr");
@@ -45,13 +49,13 @@ void TFE_OpReset(TFE_Op* op_to_reset, const char* op_or_function_name,
 
 void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetShouldStoreGraphs(true);
 }
 
 void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetShouldStoreGraphs(false);
 }
 
@@ -483,7 +487,7 @@ void TFE_ContextOptionsSetMirroringPolicy(TFE_ContextOptions* options,
 void TFE_ContextSetThreadLocalMirroringPolicy(
     TFE_Context* ctx, TFE_ContextMirroringPolicy policy) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetThreadLocalMirroringPolicy(
       static_cast<tensorflow::ContextMirroringPolicy>(policy));
 }
@@ -494,7 +498,7 @@ void TFE_ContextSetThreadLocalMirroringPolicy(
 extern TFE_ContextMirroringPolicy TFE_ContextGetMirroringPolicy(
     TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   return static_cast<TFE_ContextMirroringPolicy>(context->GetMirroringPolicy());
 }
 
@@ -530,7 +534,7 @@ void TFE_OpSetCancellationManager(TFE_Op* op,
                                   TFE_CancellationManager* cancellation_manager,
                                   TF_Status* status) {
   tensorflow::EagerOperation* operation =
-      tensorflow::OperationFromInterface(op->operation);
+      tensorflow::OperationFromInterface(tensorflow::unwrap(op));
   operation->SetCancellationManager(
       &cancellation_manager->cancellation_manager);
   status->status = tensorflow::Status::OK();
@@ -557,19 +561,19 @@ void TFE_ExecutorClearError(TFE_Executor* executor) {
 
 void TFE_ContextSetExecutorForThread(TFE_Context* ctx, TFE_Executor* executor) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   context->SetExecutorForThread(executor->executor());
 }
 
 TFE_Executor* TFE_ContextGetExecutorForThread(TFE_Context* ctx) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   return new TFE_Executor(&context->Executor());
 }
 
 void TFE_HostAddressSpace(TFE_Context* ctx, TF_Buffer* buf) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   auto address_space = tensorflow::DeviceNameUtils::AddressSpace(
       context->HostCPU()->parsed_name());
   auto str = tensorflow::DeviceNameUtils::ParsedNameToString(address_space);
@@ -585,7 +589,7 @@ void TFE_HostAddressSpace(TFE_Context* ctx, TF_Buffer* buf) {
 void TFE_ContextGetFunctionDef(TFE_Context* ctx, const char* function_name,
                                TF_Buffer* buf, TF_Status* status) {
   tensorflow::EagerContext* context =
-      tensorflow::ContextFromInterface(ctx->context);
+      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
   auto* function_def = context->FindFunctionDef(function_name);
   if (function_def == nullptr) {
     status->status = tensorflow::errors::NotFound(
@@ -611,12 +615,13 @@ TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
     dimvec[i] = static_cast<tensorflow::int64>(dims[i]);
   }
 
-  if (ctx == nullptr || ctx->context == nullptr) {
+  if (ctx == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid Context");
     return nullptr;
   }
 
-  tensorflow::AbstractTensorInterface* t = ctx->context->CreateTensor(
+  tensorflow::AbstractTensorInterface* t =
+      tensorflow::unwrap(ctx)->CreateTensor(
           static_cast<tensorflow::DataType>(dtype), dimvec);
 
   if (t == nullptr) {
@@ -630,5 +635,6 @@ TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
 
 TFE_TensorHandle* TFE_NewTensorHandleFromTensor(TFE_Context* ctx, TF_Tensor* t,
                                                 TF_Status* status) {
-  return new TFE_TensorHandle{ctx->context->CreateLocalHandle(t->tensor)};
+  return tensorflow::wrap(
+      tensorflow::unwrap(ctx)->CreateLocalHandle(t->tensor));
 }

tensorflow/c/eager/c_api_internal.h

@@ -19,13 +19,10 @@ limitations under the License.
 #include "tensorflow/c/eager/c_api.h"
 #include "tensorflow/c/eager/c_api_experimental.h"
 #include "tensorflow/c/eager/tfe_cancellation_manager_internal.h"  // IWYU pragma: export
-#include "tensorflow/c/eager/tfe_context_internal.h"  // IWYU pragma: export
 #include "tensorflow/c/eager/tfe_executor_internal.h"  // IWYU pragma: export
 #include "tensorflow/c/eager/tfe_monitoring_internal.h"  // IWYU pragma: export
 #include "tensorflow/c/eager/tfe_op_attrs_internal.h"  // IWYU pragma: export
-#include "tensorflow/c/eager/tfe_op_internal.h"  // IWYU pragma: export
 #include "tensorflow/c/eager/tfe_tensor_debug_info_internal.h"  // IWYU pragma: export
-#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"  // IWYU pragma: export
 
 // TODO(b/154564140): Move this to its own header. This requires splitting
 // c_api_experimental.h

tensorflow/c/eager/c_api_remote_test.cc

@@ -17,6 +17,7 @@ limitations under the License.
 #include "tensorflow/c/eager/c_api_experimental.h"
 #include "tensorflow/c/eager/c_api_internal.h"
 #include "tensorflow/c/eager/c_api_test_util.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/core/common_runtime/eager/eager_operation.h"
 #include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
 #include "tensorflow/core/platform/casts.h"
@@ -233,7 +234,8 @@ void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func) {
     ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU"));
     TFE_OpSetDevice(matmul, cpu_device_name.c_str(), status);
     EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
-    auto remote_arg = tensorflow::TensorHandleFromInterface(h1_task2->handle);
+    auto remote_arg =
+        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
     // The input handles should never change since they have been mirrored.
     ASSERT_FALSE(remote_arg->HasLocalMirror(nullptr));
   }
@@ -245,7 +247,8 @@ void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func) {
 
   // TODO(gjn): Add support for waiting on async local mirrors
   if (!remote && !async) {
-    auto remote_arg = tensorflow::TensorHandleFromInterface(h1_task2->handle);
+    auto remote_arg =
+        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
     // The input handles should never change since they have been mirrored.
     ASSERT_TRUE(remote_arg->HasLocalMirror(nullptr));
   }

tensorflow/c/eager/c_api_test.cc

@@ -27,6 +27,8 @@ limitations under the License.
 #include "tensorflow/c/eager/c_api_experimental.h"
 #include "tensorflow/c/eager/c_api_internal.h"
 #include "tensorflow/c/eager/c_api_test_util.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/core/common_runtime/eager/eager_operation.h"
 #include "tensorflow/core/common_runtime/eager/tensor_handle.h"
 #include "tensorflow/core/framework/function.pb.h"
@@ -416,8 +418,10 @@ void TensorHandleSilentCopy(bool async,
         hcpu, ctx, gpu_device_name.c_str(), status.get());
     ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
 
-    auto cpu_arg = tensorflow::TensorHandleFromInterface(hcpu->handle);
+    auto cpu_arg =
-    auto gpu_arg = tensorflow::TensorHandleFromInterface(hgpu->handle);
+        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hcpu));
+    auto gpu_arg =
+        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(hgpu));
     auto gpu_device = absl::get<tensorflow::Device*>(gpu_arg->device());
     ASSERT_FALSE(cpu_arg->HasLocalMirror(gpu_device));
 
@@ -1346,7 +1350,7 @@ TEST(CAPI, TestTFE_TensorHandleCopySharingUnderlyingTensorHandle) {
 tensorflow::AttrValueMap ExtractAttrs(TFE_Op* op) {
   tensorflow::AttrValueMap attr_values;
   tensorflow::EagerOperation* operation =
-      tensorflow::OperationFromInterface(op->operation);
+      tensorflow::OperationFromInterface(tensorflow::unwrap(op));
   operation->Attrs().FillAttrValueMap(&attr_values);
   return attr_values;
 }
@@ -1482,10 +1486,10 @@ TEST(CAPI, TestTFE_OpAttrsInferenceDisabledWhenNotCallingOpAddInputList) {
   TFE_TensorHandle* inputs[] = {input1, input2};
   TFE_OpAddInput(concatOp, dim, status);
   CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
-  CHECK(concatOp->operation->OpDef());
+  CHECK(tensorflow::unwrap(concatOp)->OpDef());
   TFE_OpAddInput(concatOp, inputs[0], status);
   CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
-  EXPECT_FALSE(concatOp->operation->OpDef())
+  EXPECT_FALSE(tensorflow::unwrap(concatOp)->OpDef())
       << "Inference context is still present";
   TFE_OpAddInput(concatOp, inputs[1], status);
   CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
@@ -1590,7 +1594,7 @@ TEST(CAPI, TestTFE_OpAddAttrs) {
   // There is currently no API to fetch attributes from an operation, fetching
   // happens only as an implementation detail of custom devices.
   tensorflow::EagerOperation* operation =
-      OperationFromInterface(var_op->operation);
+      OperationFromInterface(tensorflow::unwrap(var_op));
   TFE_OpAttrs attributes{&operation->Attrs()};
 
   TFE_Op* copy_op = TFE_NewOp(ctx, "VarHandleOp", status);
@@ -1606,7 +1610,7 @@ TEST(CAPI, TestTFE_OpAddAttrs) {
 
   tensorflow::AttrValueMap attr_values;
   tensorflow::EagerOperation* op =
-      tensorflow::OperationFromInterface(copy_op->operation);
+      tensorflow::OperationFromInterface(tensorflow::unwrap(copy_op));
   op->Attrs().FillAttrValueMap(&attr_values);
   EXPECT_EQ(tensorflow::DT_FLOAT, attr_values.find("dtype")->second.type());
 
@@ -1630,7 +1634,7 @@ TEST(CAPI, TestTFE_OpAttrsSerialize) {
   // There is currently no API to fetch attributes from an operation, fetching
   // happens only as an implementation detail of custom devices.
   tensorflow::EagerOperation* operation =
-      OperationFromInterface(var_op->operation);
+      OperationFromInterface(tensorflow::unwrap(var_op));
   TFE_OpAttrs attributes{&operation->Attrs()};
 
   TF_Buffer* serialized_attr_values = TF_NewBuffer();
@@ -1657,7 +1661,7 @@ TEST(CAPI, TestTFE_OpAttrsSerialize) {
 
   tensorflow::AttrValueMap attr_values;
   tensorflow::EagerOperation* op =
-      tensorflow::OperationFromInterface(var_op_2->operation);
+      tensorflow::OperationFromInterface(tensorflow::unwrap(var_op_2));
   op->Attrs().FillAttrValueMap(&attr_values);
   EXPECT_EQ(tensorflow::DT_INT64, attr_values.find("dtype")->second.type());
 

tensorflow/c/eager/dlpack.cc

@@ -16,8 +16,10 @@ limitations under the License.
 #include "tensorflow/c/eager/dlpack.h"
 
 #include "include/dlpack/dlpack.h"  // from @dlpack
-#include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/c_api.h"
-#include "tensorflow/c/tf_status_helper.h"
+#include "tensorflow/c/eager/c_api_experimental.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
+#include "tensorflow/c/tf_status_internal.h"
 #include "tensorflow/core/common_runtime/eager/tensor_handle.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_reference.h"
@@ -41,12 +43,12 @@ struct TfDlManagedTensorCtx {
 
 // Gets tensor from eager tensor handle.
 const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
-  if (h == nullptr || h->handle == nullptr) {
+  if (h == nullptr) {
     status->status = tensorflow::errors::InvalidArgument("Invalid handle");
     return nullptr;
   }
   tensorflow::TensorHandle* handle =
-      tensorflow::TensorHandleFromInterface(h->handle);
+      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
   if (handle->Type() != TensorHandle::LOCAL) {
     status->status = tensorflow::errors::InvalidArgument(
         "DLPack doesn't support ", handle->TypeString(), " tensor");
@@ -107,7 +109,7 @@ DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {
 // Gets DLPack's DLContext from eager tensor handle.
 DLContext GetDlContext(TFE_TensorHandle* h, TF_Status* status) {
   DLContext ctx;
-  const char* device_name = h->handle->DeviceName(&status->status);
+  const char* device_name = tensorflow::unwrap(h)->DeviceName(&status->status);
   DeviceNameUtils::ParsedName parsed_name;
   tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);
   std::string device_type = parsed_name.type;

tensorflow/c/eager/tfe_context_internal.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_C_EAGER_TFE_CONTEXT_INTERNAL_H_
 #define TENSORFLOW_C_EAGER_TFE_CONTEXT_INTERNAL_H_
 
+#include "tensorflow/c/conversion_macros.h"
 #include "tensorflow/c/eager/context_interface.h"
 
 // Wraps a pointer to a context implementation.
@@ -23,8 +24,12 @@ limitations under the License.
 // interface cannot destruct the underlying context object. Instead, call
 // TFE_DeleteContext who calls Release() on the context pointer and deletes
 // the TFE_Context structure.
-struct TFE_Context {
+typedef struct TFE_Context TFE_Context;
-  tensorflow::AbstractContextInterface* context;
+
-};
+namespace tensorflow {
+
+DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractContextInterface, TFE_Context);
+
+}  // namespace tensorflow
 
 #endif  // TENSORFLOW_C_EAGER_TFE_CONTEXT_INTERNAL_H_

tensorflow/c/eager/tfe_op_attrs_internal.h

@@ -23,14 +23,15 @@ limitations under the License.
 #include <string>
 #include <vector>
 
-#include "tensorflow/c/eager/tfe_context_internal.h"
-#include "tensorflow/c/eager/tfe_op_internal.h"
 #include "tensorflow/c/tf_status.h"
 #include "tensorflow/core/common_runtime/eager/attr_builder.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
 
 // An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways
 // that sometimes do not require serialization.
+typedef struct TFE_Context TFE_Context;
+typedef struct TFE_Op TFE_Op;
+
 struct TFE_OpAttrs {
   explicit TFE_OpAttrs() : attributes(nullptr) {}
 

tensorflow/c/eager/tfe_op_internal.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_C_EAGER_TFE_OP_INTERNAL_H_
 #define TENSORFLOW_C_EAGER_TFE_OP_INTERNAL_H_
 
+#include "tensorflow/c/conversion_macros.h"
 #include "tensorflow/c/eager/operation_interface.h"
 
 // Wraps a pointer to an operation implementation.
@@ -23,8 +24,13 @@ limitations under the License.
 // interface cannot destruct the underlying operation object. Instead, call
 // TFE_DeleteOp who calls Release() on the operation pointer and deletes
 // the TFE_Op structure.
-struct TFE_Op {
+typedef struct TFE_Op TFE_Op;
-  tensorflow::AbstractOperationInterface* operation;
+
-};
+namespace tensorflow {
+
+DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractOperationInterface, TFE_Op);
+DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractOperationInterface*, TFE_Op*);
+
+}  // namespace tensorflow
 
 #endif  // TENSORFLOW_C_EAGER_TFE_OP_INTERNAL_H_

tensorflow/c/eager/tfe_tensorhandle_internal.h

@@ -15,6 +15,7 @@ limitations under the License.
 #ifndef TENSORFLOW_C_EAGER_TFE_TENSORHANDLE_INTERNAL_H_
 #define TENSORFLOW_C_EAGER_TFE_TENSORHANDLE_INTERNAL_H_
 
+#include "tensorflow/c/conversion_macros.h"
 #include "tensorflow/c/eager/tensor_handle_interface.h"
 
 // Wraps a pointer to a tensor handle implementation.
@@ -23,8 +24,15 @@ limitations under the License.
 // interface cannot destruct the underlying handle object. Instead, call
 // TFE_DeleteTensorHandle who calls Release() on the handle pointer and deletes
 // the TFE_TensorHandle structure.
-struct TFE_TensorHandle {
+typedef struct TFE_TensorHandle TFE_TensorHandle;
-  tensorflow::AbstractTensorHandleInterface* handle;
+
-};
+namespace tensorflow {
+
+DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractTensorHandleInterface,
+                            TFE_TensorHandle);
+DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractTensorHandleInterface*,
+                            TFE_TensorHandle*);
+
+}  // namespace tensorflow
 
 #endif  // TENSORFLOW_C_EAGER_TFE_TENSORHANDLE_INTERNAL_H_

tensorflow/c/experimental/saved_model/internal/BUILD

@@ -22,13 +22,6 @@ package(
     licenses = ["notice"],  # Apache 2.0
 )
 
-cc_library(
-    name = "conversion_macros",
-    hdrs = [
-        "conversion_macros.h",
-    ],
-)
-
 cc_library(
     name = "concrete_function",
     srcs = [
@@ -51,6 +44,7 @@ cc_library(
         "//tensorflow/c:c_api_macros",
         "//tensorflow/c/eager:c_api",
         "//tensorflow/c/eager:c_api_internal",
+        "//tensorflow/c/eager:tfe_op_internal",
         "//tensorflow/c/experimental/saved_model/core:concrete_function",
         "//tensorflow/c/experimental/saved_model/core:function_metadata",
     ],
@@ -93,7 +87,7 @@ cc_library(
         "concrete_function_type.h",
     ],
     deps = [
-        ":conversion_macros",
+        "//tensorflow/c:conversion_macros",
         "//tensorflow/c/experimental/saved_model/core:concrete_function",
     ],
 )
@@ -123,7 +117,7 @@ cc_library(
         "function_metadata_type.h",
     ],
     deps = [
-        ":conversion_macros",
+        "//tensorflow/c:conversion_macros",
         "//tensorflow/c/experimental/saved_model/core:function_metadata",
     ],
 )

tensorflow/c/experimental/saved_model/internal/concrete_function.cc

@@ -16,6 +16,7 @@ limitations under the License.
 #include "tensorflow/c/experimental/saved_model/public/concrete_function.h"
 
 #include "tensorflow/c/eager/c_api_unified_experimental.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
 #include "tensorflow/c/experimental/saved_model/core/concrete_function.h"
 #include "tensorflow/c/experimental/saved_model/core/function_metadata.h"
 #include "tensorflow/c/experimental/saved_model/internal/concrete_function_type.h"
@@ -24,7 +25,8 @@ limitations under the License.
 extern "C" {
 
 TF_FunctionMetadata* TF_ConcreteFunctionGetMetadata(TF_ConcreteFunction* func) {
-  return tensorflow::wrap(&tensorflow::unwrap(func)->GetFunctionMetadata());
+  return tensorflow::wrap(const_cast<tensorflow::FunctionMetadata*>(
+      &tensorflow::unwrap(func)->GetFunctionMetadata()));
 }
 
 TF_OutputList* TF_ConcreteFunctionGetCaptures(TF_ConcreteFunction* func) {
@@ -34,7 +36,7 @@ TF_OutputList* TF_ConcreteFunctionGetCaptures(TF_ConcreteFunction* func) {
 }
 
 TFE_Op* TF_ConcreteFunctionGetCallOp(TF_ConcreteFunction* func) {
-  return new TFE_Op{tensorflow::unwrap(func)->GetCallOp()};
+  return tensorflow::wrap(tensorflow::unwrap(func)->GetCallOp());
 }
 
 }  // end extern "C"

tensorflow/c/experimental/saved_model/internal/concrete_function_type.h

@@ -16,8 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_CONCRETE_FUNCTION_TYPE_H_
 #define TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_CONCRETE_FUNCTION_TYPE_H_
 
+#include "tensorflow/c/conversion_macros.h"
 #include "tensorflow/c/experimental/saved_model/core/concrete_function.h"
-#include "tensorflow/c/experimental/saved_model/internal/conversion_macros.h"
 
 // Internal structures used by the SavedModel C API. These are likely to change
 // and should not be depended on.

tensorflow/c/experimental/saved_model/internal/conversion_macros.h

@@ -1,28 +0,0 @@
-/* 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_EXPERIMENTAL_SAVED_MODEL_INTERNAL_CONVERSION_MACROS_H_
-#define TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_CONVERSION_MACROS_H_
-
-#define DEFINE_CONVERSION_FUNCTIONS(cpp_impl, wrapper)             \
-  inline cpp_impl *unwrap(wrapper *w) {                            \
-    return reinterpret_cast<cpp_impl *>(w);                        \
-  }                                                                \
-                                                                   \
-  inline wrapper *wrap(const cpp_impl *i) {                        \
-    return reinterpret_cast<wrapper *>(const_cast<cpp_impl *>(i)); \
-  }
-
-#endif  // TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_CONVERSION_MACROS_H_

tensorflow/c/experimental/saved_model/internal/function_metadata_type.h

@@ -16,8 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_FUNCTION_METADATA_TYPE_H_
 #define TENSORFLOW_C_EXPERIMENTAL_SAVED_MODEL_INTERNAL_FUNCTION_METADATA_TYPE_H_
 
+#include "tensorflow/c/conversion_macros.h"
 #include "tensorflow/c/experimental/saved_model/core/function_metadata.h"
-#include "tensorflow/c/experimental/saved_model/internal/conversion_macros.h"
 
 typedef struct TF_FunctionMetadata TF_FunctionMetadata;
 

tensorflow/c/experimental/saved_model/internal/saved_model_api.cc

@@ -36,7 +36,8 @@ TF_SavedModel* TF_LoadSavedModel(const char* dirname, TFE_Context* ctx,
   std::string saved_model_dir(dirname);
 
   std::unique_ptr<tensorflow::SavedModelAPI> result =
-      ctx->context->LoadSavedModelAPI(dirname, absl::nullopt, &status->status);
+      tensorflow::unwrap(ctx)->LoadSavedModelAPI(dirname, absl::nullopt,
+                                                 &status->status);
   if (!status->status.ok()) {
     return nullptr;
   }
@@ -54,7 +55,7 @@ TF_SavedModel* TF_LoadSavedModelWithTags(const char* dirname, TFE_Context* ctx,
   }
 
   std::unique_ptr<tensorflow::SavedModelAPI> result =
-      ctx->context->LoadSavedModelAPI(dirname, std::move(tagset),
+      tensorflow::unwrap(ctx)->LoadSavedModelAPI(dirname, std::move(tagset),
                                                  &status->status);
   if (!status->status.ok()) {
     return nullptr;

tensorflow/python/BUILD

@@ -903,7 +903,8 @@ cc_library(
         ":safe_ptr",
         "//tensorflow/c:tf_status_helper",
         "//tensorflow/c/eager:c_api",
-        "//tensorflow/c/eager:c_api_internal",
+        "//tensorflow/c/eager:tfe_context_internal",
+        "//tensorflow/c/eager:tfe_tensorhandle_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",
@@ -1007,7 +1008,8 @@ cc_library(
         ":safe_ptr",
         "//tensorflow/c:tensor_interface",
         "//tensorflow/c:tf_tensor_internal",
-        "//tensorflow/c/eager:c_api_internal",
+        "//tensorflow/c/eager:tfe_context_internal",
+        "//tensorflow/c/eager:tfe_tensorhandle_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//third_party/python_runtime:headers",

tensorflow/python/eager/BUILD

@@ -40,6 +40,9 @@ cc_library(
         "//tensorflow/c/eager:c_api_internal",
         "//tensorflow/c/eager:dlpack",
         "//tensorflow/c/eager:tape",
+        "//tensorflow/c/eager:tfe_context_internal",
+        "//tensorflow/c/eager:tfe_op_internal",
+        "//tensorflow/c/eager:tfe_tensorhandle_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:protos_all_cc",

tensorflow/python/eager/pywrap_tensor_conversion.cc

@@ -17,7 +17,7 @@ limitations under the License.
 
 #include "absl/container/flat_hash_map.h"
 #include "absl/hash/hash.h"
-#include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/core/lib/monitoring/counter.h"
 #include "tensorflow/core/platform/logging.h"
 
@@ -41,7 +41,7 @@ TFE_TensorHandle* TFE_TensorHandleCache::Lookup(
     PyObject* value, tensorflow::DataType dtype,
     absl::string_view device_name) const {
   CHECK_NOTNULL(value);
-  const auto& it = cache.find(Key{PyObjectPtr{value}, dtype, device_name});
+  const auto it = cache.find(Key{PyObjectPtr{value}, dtype, device_name});
   if (it == cache.end()) {
     scalar_cache_misses->GetCell()->IncrementBy(1);
     return nullptr;
@@ -49,7 +49,7 @@ TFE_TensorHandle* TFE_TensorHandleCache::Lookup(
 
   scalar_cache_hits->GetCell()->IncrementBy(1);
   auto* h = it->second;
-  return new TFE_TensorHandle{h->handle->Copy()};
+  return tensorflow::wrap(tensorflow::unwrap(h)->Copy());
 }
 
 void TFE_TensorHandleCache::Insert(PyObject* value, tensorflow::DataType dtype,
@@ -57,7 +57,7 @@ void TFE_TensorHandleCache::Insert(PyObject* value, tensorflow::DataType dtype,
                                    TFE_TensorHandle* h) {
   Py_INCREF(value);
   cache.emplace(Key{PyObjectPtr{value}, dtype, device_name},
-                new TFE_TensorHandle{h->handle->Copy()});
+                tensorflow::wrap(tensorflow::unwrap(h)->Copy()));
 }
 
 void TFE_TensorHandleCache::Clear() {

tensorflow/python/eager/pywrap_tfe.h

@@ -379,7 +379,7 @@ void TFE_Py_EnableInteractivePythonLogging();
 // Py_None.
 //
 // This function is not thread-safe.
-PyObject* TFE_Py_SetEagerContext(PyObject* python_context);
+PyObject* TFE_Py_SetEagerContext(PyObject* py_context);
 
 // Returns the current eager Context object (defined in eager/context.py)
 // that was last set using TFE_Py_SetEagerContext.

tensorflow/python/eager/pywrap_tfe_src.cc

@@ -24,6 +24,9 @@ limitations under the License.
 #include "tensorflow/c/eager/c_api.h"
 #include "tensorflow/c/eager/c_api_internal.h"
 #include "tensorflow/c/eager/tape.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_op_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/c/tf_status.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/errors.h"
@@ -80,9 +83,10 @@ TFE_Op* GetOp(TFE_Context* ctx, const char* op_or_function_name,
               const char* raw_device_name, TF_Status* status) {
   auto op = ReleaseThreadLocalOp(ctx);
   if (!op) {
-    op.reset(new TFE_Op{ctx->context->CreateOperation()});
+    op.reset(tensorflow::wrap(tensorflow::unwrap(ctx)->CreateOperation()));
   }
-  status->status = op->operation->Reset(op_or_function_name, raw_device_name);
+  status->status =
+      tensorflow::unwrap(op.get())->Reset(op_or_function_name, raw_device_name);
   if (!status->status.ok()) {
     op.reset();
   }
@@ -91,7 +95,7 @@ TFE_Op* GetOp(TFE_Context* ctx, const char* op_or_function_name,
 
 void ReturnOp(TFE_Context* ctx, TFE_Op* op) {
   if (op) {
-    op->operation->Clear();
+    tensorflow::unwrap(op)->Clear();
     thread_local_eager_operation_map[ctx].reset(op);
   }
 }
@@ -1500,7 +1504,7 @@ static PyTypeObject TFE_Py_Tape_Type = {
     sizeof(TFE_Py_Tape),                          /* tp_basicsize */
     0,                                            /* tp_itemsize */
     &TFE_Py_Tape_Delete,                          /* tp_dealloc */
-    0,                                            /* tp_print */
+    nullptr,                                      /* tp_print */
     nullptr,                                      /* tp_getattr */
     nullptr,                                      /* tp_setattr */
     nullptr,                                      /* tp_reserved */
@@ -1538,7 +1542,7 @@ static PyTypeObject TFE_Py_ForwardAccumulator_Type = {
     sizeof(TFE_Py_ForwardAccumulator),                      /* tp_basicsize */
     0,                                                      /* tp_itemsize */
     &TFE_Py_ForwardAccumulatorDelete,                       /* tp_dealloc */
-    0,                                                      /* tp_print */
+    nullptr,                                                /* tp_print */
     nullptr,                                                /* tp_getattr */
     nullptr,                                                /* tp_setattr */
     nullptr,                                                /* tp_reserved */
@@ -1573,7 +1577,7 @@ static PyTypeObject TFE_Py_VariableWatcher_Type = {
     sizeof(TFE_Py_VariableWatcher),                          /* tp_basicsize */
     0,                                                       /* tp_itemsize */
     &TFE_Py_VariableWatcher_Delete,                          /* tp_dealloc */
-    0,                                                       /* tp_print */
+    nullptr,                                                 /* tp_print */
     nullptr,                                                 /* tp_getattr */
     nullptr,                                                 /* tp_setattr */
     nullptr,                                                 /* tp_reserved */
@@ -1990,21 +1994,22 @@ bool ListContainsNone(PyObject* list) {
 
 static PyTapeTensor TapeTensorFromTensor(PyObject* tensor) {
   if (EagerTensor_CheckExact(tensor)) {
-    TFE_TensorHandle* t = EagerTensor_Handle(tensor);
+    tensorflow::AbstractTensorHandleInterface* handle =
+        tensorflow::unwrap(EagerTensor_Handle(tensor));
     tensorflow::int64 id = PyEagerTensor_ID(tensor);
     tensorflow::DataType dtype =
-        static_cast<tensorflow::DataType>(t->handle->DataType());
+        static_cast<tensorflow::DataType>(handle->DataType());
     if (dtype == tensorflow::DT_VARIANT) {
       return PyTapeTensor(id, dtype, tensor);
     }
 
     tensorflow::TensorShape tensor_shape;
     int num_dims;
-    tensorflow::Status status = t->handle->NumDims(&num_dims);
+    tensorflow::Status status = handle->NumDims(&num_dims);
     if (status.ok()) {
       for (int i = 0; i < num_dims; ++i) {
         tensorflow::int64 dim_size;
-        status = t->handle->Dim(i, &dim_size);
+        status = handle->Dim(i, &dim_size);
         if (!status.ok()) break;
         tensor_shape.AddDim(dim_size);
       }
@@ -3511,7 +3516,7 @@ PyObject* TFE_Py_FastPathExecute_C(PyObject* args) {
     return nullptr;
   }
 
-  const tensorflow::OpDef* op_def = op->operation->OpDef();
+  const tensorflow::OpDef* op_def = tensorflow::unwrap(op)->OpDef();
   if (op_def == nullptr) return nullptr;
 
   if (args_size < kFastPathExecuteInputStartIndex + op_def->input_arg_size()) {
@@ -3850,14 +3855,15 @@ tensorflow::Status TFE_Py_EncodeTensor(PyObject* arg,
                                        bool include_tensor_ranks_only,
                                        EncodeResult* result) {
   if (EagerTensor_CheckExact(arg)) {
-    TFE_TensorHandle* t = EagerTensor_Handle(arg);
+    tensorflow::AbstractTensorHandleInterface* handle =
+        tensorflow::unwrap(EagerTensor_Handle(arg));
 
     absl::StrAppend(&result->str, kDType,
-                    static_cast<tensorflow::DataType>(t->handle->DataType()));
+                    static_cast<tensorflow::DataType>(handle->DataType()));
     absl::StrAppend(&result->str, kShape);
 
     int num_dims;
-    tensorflow::Status status = t->handle->NumDims(&num_dims);
+    tensorflow::Status status = handle->NumDims(&num_dims);
     if (!status.ok()) return status;
 
     if (include_tensor_ranks_only) {
@@ -3865,7 +3871,7 @@ tensorflow::Status TFE_Py_EncodeTensor(PyObject* arg,
     } else {
       for (int i = 0; i < num_dims; ++i) {
         tensorflow::int64 dim_size;
-        status = t->handle->Dim(i, &dim_size);
+        status = handle->Dim(i, &dim_size);
         if (!status.ok()) return status;
         absl::StrAppend(&result->str, dim_size, kShapeDelim);
       }

tensorflow/python/lib/core/py_func.cc

@@ -26,7 +26,8 @@ limitations under the License.
 
 #include "numpy/arrayobject.h"
 #include "tensorflow/c/eager/c_api.h"
-#include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/c/tf_status_helper.h"
 #include "tensorflow/core/common_runtime/eager/context.h"
 #include "tensorflow/core/common_runtime/eager/tensor_handle.h"
@@ -95,8 +96,8 @@ Status MakeArgTuple(const PyCall* call, EagerContext* ctx, PyObject** tuple) {
     if (call->eager) {
       Tensor t = call->ins[i];
       arg = EagerTensorFromHandle(
-          new TFE_TensorHandle{TensorHandle::CreateLocalHandle(
+          tensorflow::wrap(TensorHandle::CreateLocalHandle(
-              std::move(t), ctx->CanonicalDevice(device), nullptr, ctx)});
+              std::move(t), ctx->CanonicalDevice(device), nullptr, ctx)));
       if (arg == nullptr) {
         Py_DECREF(lst);
         return errors::Internal("Unable to procure EagerTensor from Tensor.");
@@ -146,7 +147,7 @@ tensorflow::Status ExtractTensorFromEagerTensor(const PyObject* eager_tensor,
                                                 const Device* expected_device,
                                                 const Tensor** output_tensor) {
   tensorflow::TensorHandle* handle = tensorflow::TensorHandleFromInterface(
-      EagerTensor_Handle(eager_tensor)->handle);
+      tensorflow::unwrap(EagerTensor_Handle(eager_tensor)));
   if (VariantDeviceIsCustom(handle->device())) {
     return errors::Unimplemented(
         "Custom devices are currently not supported with PyFuncs.");
@@ -196,7 +197,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) {
     TFE_Context* ctx = reinterpret_cast<TFE_Context*>(PyCapsule_GetPointer(
         PyObject_GetAttrString(trampoline, "_ctx"), nullptr));
     CHECK_NE(ctx, nullptr);
-    EagerContext* context = ContextFromInterface(ctx->context);
+    EagerContext* context = ContextFromInterface(tensorflow::unwrap(ctx));
     TF_RETURN_IF_ERROR(MakeArgTuple(call, context, &args));
     new_executor.reset(new EagerExecutor(call->eager_async));
     old_executor = &context->Executor();
@@ -236,7 +237,7 @@ Status DoCallPyFunc(PyCall* call, bool* out_log_on_error) {
   if (new_executor != nullptr) {
     TFE_Context* ctx = reinterpret_cast<TFE_Context*>(PyCapsule_GetPointer(
         PyObject_GetAttrString(trampoline, "_ctx"), nullptr));
-    EagerContext* context = ContextFromInterface(ctx->context);
+    EagerContext* context = ContextFromInterface(tensorflow::unwrap(ctx));
     s.Update(new_executor->WaitForAllPendingNodes());
     context->SetExecutorForThread(old_executor);
   }

tensorflow/python/lib/core/py_seq_tensor.cc

@@ -15,7 +15,8 @@ limitations under the License.
 
 #include "tensorflow/python/lib/core/py_seq_tensor.h"
 
-#include "tensorflow/c/eager/c_api_internal.h"
+#include "tensorflow/c/eager/tfe_context_internal.h"
+#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
 #include "tensorflow/c/tensor_interface.h"
 #include "tensorflow/c/tf_tensor_internal.h"
 #include "tensorflow/core/framework/tensor.h"
@@ -305,7 +306,7 @@ struct Converter {
         }
       }
     }
-    *h = new TFE_TensorHandle{ctx->context->CreateLocalHandle(t)};
+    *h = tensorflow::wrap(tensorflow::unwrap(ctx)->CreateLocalHandle(t));
     t->Release();
     return Status::OK();
   }
@@ -316,12 +317,12 @@ struct Converter {
 template <>
 struct ConverterTraits<int64> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, int64 value) {
-    return ctx->context->CreateInt64Scalar(value);
+    return tensorflow::unwrap(ctx)->CreateInt64Scalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_INT64, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_INT64, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, int64* out) {
@@ -356,12 +357,12 @@ typedef Converter<int64> Int64Converter;
 template <>
 struct ConverterTraits<uint64> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, uint64 value) {
-    return ctx->context->CreateUint64Scalar(value);
+    return tensorflow::unwrap(ctx)->CreateUint64Scalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_UINT64, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_UINT64, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, uint64* out) {
@@ -393,12 +394,12 @@ typedef Converter<uint64> UInt64Converter;
 template <>
 struct ConverterTraits<int32> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, int32 value) {
-    return ctx->context->CreateInt32Scalar(value);
+    return tensorflow::unwrap(ctx)->CreateInt32Scalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_INT32, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_INT32, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, int32* out) {
@@ -500,12 +501,12 @@ static const char* ConvertOneFloat(PyObject* v, T* out) {
 template <>
 struct ConverterTraits<float> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, float value) {
-    return ctx->context->CreateFloatScalar(value);
+    return tensorflow::unwrap(ctx)->CreateFloatScalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_FLOAT, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_FLOAT, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, float* out) {
@@ -516,12 +517,12 @@ struct ConverterTraits<float> {
 template <>
 struct ConverterTraits<double> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, double value) {
-    return ctx->context->CreateDoubleScalar(value);
+    return tensorflow::unwrap(ctx)->CreateDoubleScalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_DOUBLE, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_DOUBLE, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, double* out) {
@@ -536,12 +537,12 @@ template <>
 struct ConverterTraits<Eigen::half> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx,
                                                Eigen::half value) {
-    return ctx->context->CreateHalfScalar(value);
+    return tensorflow::unwrap(ctx)->CreateHalfScalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_HALF, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_HALF, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, Eigen::half* out) {
@@ -557,12 +558,12 @@ template <>
 struct ConverterTraits<tstring> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx,
                                                tstring value) {
-    return ctx->context->CreateStringScalar(value);
+    return tensorflow::unwrap(ctx)->CreateStringScalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_STRING, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_STRING, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, tstring* out) {
@@ -624,12 +625,12 @@ template <>
 struct ConverterTraits<complex128> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx,
                                                complex128 value) {
-    return ctx->context->CreateComplex128Scalar(value);
+    return tensorflow::unwrap(ctx)->CreateComplex128Scalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_COMPLEX128, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_COMPLEX128, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, complex128* out) {
@@ -652,12 +653,12 @@ typedef Converter<complex128> Complex128Converter;
 template <>
 struct ConverterTraits<bool> {
   static AbstractTensorInterface* CreateScalar(TFE_Context* ctx, bool value) {
-    return ctx->context->CreateBoolScalar(value);
+    return tensorflow::unwrap(ctx)->CreateBoolScalar(value);
   }
 
   static AbstractTensorInterface* CreateTensor(
       TFE_Context* ctx, absl::Span<const int64> dim_sizes) {
-    return ctx->context->CreateTensor(DT_BOOL, dim_sizes);
+    return tensorflow::unwrap(ctx)->CreateTensor(DT_BOOL, dim_sizes);
   }
 
   static const char* ConvertScalar(PyObject* v, bool* out) {
@@ -692,7 +693,7 @@ TFE_TensorHandle* NumpyToTFE_TensorHandle(TFE_Context* ctx, PyObject* obj) {
   }
 
   TensorInterface t(std::move(tensor));
-  return new TFE_TensorHandle{ctx->context->CreateLocalHandle(&t)};
+  return tensorflow::wrap(tensorflow::unwrap(ctx)->CreateLocalHandle(&t));
 }
 
 }  // namespace
@@ -877,7 +878,7 @@ TFE_TensorHandle* PySeqToTFE_TensorHandle(TFE_Context* ctx, PyObject* obj,
       Tensor tensor(requested_dtype == DT_INVALID ? DT_FLOAT : requested_dtype,
                     TensorShape(state.inferred_shape));
       TensorInterface t(std::move(tensor));
-      return new TFE_TensorHandle{ctx->context->CreateLocalHandle(&t)};
+      return tensorflow::wrap(tensorflow::unwrap(ctx)->CreateLocalHandle(&t));
     }
 
     default:

tensorflow/python/tfe_wrapper.cc

@@ -270,7 +270,6 @@ static py::object TFE_ClearScalarCache() {
 // are only assigning this to functions that return opaque types.
 
 PYBIND11_MODULE(_pywrap_tfe, m) {
-  py::class_<TFE_Context> TFE_Context_class(m, "TFE_Context");
   py::class_<TFE_Executor> TFE_Executor_class(m, "TFE_Executor");
   py::class_<TFE_ContextOptions> TFE_ContextOptions_class(m,
                                                           "TFE_ContextOptions");
@@ -760,7 +759,9 @@ PYBIND11_MODULE(_pywrap_tfe, m) {
   m.def("TFE_ContextStartStep", [](py::handle& o) {
     TFE_ContextStartStep(tensorflow::InputTFE_Context(o.ptr()));
   });
-  m.def("TFE_ContextEndStep", &TFE_ContextEndStep);
+  m.def("TFE_ContextEndStep", [](py::handle& o) {
+    TFE_ContextEndStep(tensorflow::InputTFE_Context(o.ptr()));
+  });
   m.def("TFE_Py_RegisterVSpace", [](const py::handle& o) {
     return tensorflow::PyoOrThrow(TFE_Py_RegisterVSpace(o.ptr()));
   });