android-12.0.0_r34/s

/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/distributed_runtime/eager/eager_service_impl.h"

#include "absl/container/fixed_array.h"
#include "absl/memory/memory.h"
#include "absl/types/optional.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/common_runtime/eager/context.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/common_runtime/eager/execute.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/common_runtime/process_util.h"
#include "tensorflow/core/distributed_runtime/eager/cluster_function_library_runtime.h"
#include "tensorflow/core/distributed_runtime/eager/remote_mgr.h"
#include "tensorflow/core/distributed_runtime/eager/remote_tensor_handle.h"
#include "tensorflow/core/distributed_runtime/message_wrappers.h"
#include "tensorflow/core/distributed_runtime/rpc/rpc_rendezvous_mgr.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/distributed_runtime/session_mgr.h"
#include "tensorflow/core/distributed_runtime/worker_cache.h"
#include "tensorflow/core/distributed_runtime/worker_cache_wrapper.h"
#include "tensorflow/core/distributed_runtime/worker_env.h"
#include "tensorflow/core/framework/rendezvous.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/cpu_info.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/host_info.h"
#include "tensorflow/core/platform/mutex.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/profiler/lib/traceme.h"
#include "tensorflow/core/protobuf/error_codes.pb.h"

namespace tensorflow {
namespace eager {

namespace {
Status GetNumRetvals(tensorflow::EagerContext* context, const string& op_name,
                     const google::protobuf::Map<string, tensorflow::AttrValue>& attrs,
                     int* num_retvals) {
  const tensorflow::OpRegistrationData* op_reg_data = nullptr;
  auto status = tensorflow::OpRegistry::Global()->LookUp(op_name, &op_reg_data);
  if (errors::IsNotFound(status)) {
    status = context->FindFunctionOpData(op_name, &op_reg_data);
  }
  TF_RETURN_IF_ERROR(status);

  const tensorflow::OpDef& op_def = op_reg_data->op_def;

  for (const auto& output_arg : op_def.output_arg()) {
    if (!output_arg.number_attr().empty()) {
      auto iter = attrs.find(output_arg.number_attr());
      if (iter == attrs.end()) {
        return errors::InvalidArgument("Unable to find number_attr ",
                                       output_arg.number_attr(),
                                       " for Op: ", op_name);
      }
      *num_retvals += iter->second.i();
    } else if (!output_arg.type_list_attr().empty()) {
      auto iter = attrs.find(output_arg.type_list_attr());
      if (iter == attrs.end()) {
        return errors::InvalidArgument("Unable to find type_list_attr ",
                                       output_arg.type_list_attr(),
                                       " for Op: ", op_name);
      }
      *num_retvals += iter->second.list().type_size();
    } else {
      *num_retvals += 1;
    }
  }

  return Status::OK();
}

Status GetEagerOperationAndNumRetvals(const Operation& operation,
                                      EagerContext* eager_context,
                                      EagerExecutor* eager_executor,
                                      EagerOperation* eager_op,
                                      int* num_retvals) {
  const char* name = operation.name().c_str();  // Shorthand
  absl::optional<tensorflow::EagerRemoteFunctionParams> remote_func_params =
      absl::nullopt;
  if (operation.is_function()) {
    if (operation.is_component_function()) {
      remote_func_params = {operation.id(), operation.func_step_id()};
    } else {
      remote_func_params = {operation.id(), absl::nullopt};
    }
  }
  TF_RETURN_IF_ERROR(eager_op->Reset(name, operation.device().c_str(), false,
                                     eager_executor, remote_func_params));

  {
    profiler::TraceMe activity("EagerService:RemoteTensorHandleInternal",
                               profiler::TraceMeLevel::kVerbose);
    for (const auto& input : operation.op_inputs()) {
      tensorflow::TensorHandle* handle;
      if (input.has_remote_handle()) {
        TF_RETURN_IF_ERROR(
            eager_context->RemoteMgr()->DeserializeRemoteTensorHandle(
                input.remote_handle(), &handle));
        TF_RETURN_IF_ERROR(eager_op->AddInput(handle));
      } else {
        Tensor tensor;
        if (!ParseTensorProtoToTensor(input.tensor(), &tensor)) {
          return errors::InvalidArgument("Invalid TensorProto: ",
                                         input.tensor().DebugString());
        } else {
          handle = TensorHandle::CreateLocalHandle(std::move(tensor), nullptr,
                                                   nullptr, eager_context);
          TF_RETURN_IF_ERROR(eager_op->AddInput(handle));
        }
      }
      // Unref handle since it has a ref as an input now.
      handle->Unref();
    }
  }

  for (const auto& attr : operation.attrs()) {
    eager_op->MutableAttrs()->Set(attr.first, attr.second);
  }

  // TODO(nareshmodi): Consider caching this.
  return GetNumRetvals(eager_context, operation.name(), operation.attrs(),
                       num_retvals);
}

Status TensorHandleProto(TensorHandle* handle, TensorProto* proto) {
  const tensorflow::Tensor* t = nullptr;
  TF_RETURN_IF_ERROR(handle->Tensor(&t));
  t->AsProtoTensorContent(proto);
  return Status::OK();
}

Status TensorHandleShape(TensorHandle* handle, TensorShapeProto* proto) {
  const tensorflow::Tensor* t = nullptr;

  // TODO(nareshmodi): This call makes async calls sync calls. Fix this.
  if (handle->Type() == TensorHandle::LOCAL) {
    TF_RETURN_IF_ERROR(handle->Tensor(&t));

    t->shape().AsProto(proto);
  } else {
    TensorShape shape;
    TF_RETURN_IF_ERROR(handle->Shape(&shape));
    shape.AsProto(proto);
  }

  return Status::OK();
}

Status AddOpRetvalsToResponse(
    EagerContext* eager_context, int op_id, int num_retvals,
    const std::vector<int32>& output_nums, TensorHandle** retvals,
    std::function<TensorProto*()> add_tensor_proto_fn,
    std::function<TensorShapeProto*()> add_shape_proto_fn,
    std::function<string*()> add_device_fn = nullptr) {
  if (op_id == kInvalidRemoteOpId) {
    // Copy the output tensors back along with the response, since the op id
    // is invalid which cannot be added to RemoteMgr.
    for (int i = 0; i < num_retvals; i++) {
      TF_RETURN_IF_ERROR(TensorHandleProto(retvals[i], add_tensor_proto_fn()));
      retvals[i]->Unref();
    }
  } else {
    for (int i = 0; i < num_retvals; i++) {
      TF_RETURN_IF_ERROR(TensorHandleShape(retvals[i], add_shape_proto_fn()));
      if (add_device_fn) {
        Device* device = retvals[i]->device();
        *add_device_fn() = device ? device->name() : "";
      }
      if (retvals[i]->Type() == TensorHandle::REMOTE) {
        retvals[i]->Unref();
      } else {
        const int output_num = output_nums.empty() ? i : output_nums.at(i);
        eager_context->RemoteMgr()->AddOperationOutput(retvals[i], op_id,
                                                       output_num);
      }
    }
  }
  return Status::OK();
}
}  // namespace

Status EagerServiceImpl::CreateContext(const CreateContextRequest* request,
                                       CreateContextResponse* response) {
  {
    mutex_lock l(contexts_mu_);
    auto context_it = contexts_.find(request->context_id());
    if (context_it != contexts_.end()) {
      if (request->context_view_id() <
          context_it->second->Context()->GetContextViewId()) {
        return errors::InvalidArgument("EagerService:CreateContext failed. ",
                                       "Context id: <", request->context_id(),
                                       "> already exists.");
      } else {
        // For existing context with a stale context_view_id, close the old one
        // and recreate with new view id. This is likely due to the worker
        // disconnected and then reconnected after one or more cluster updates.
        context_it->second->Unref();
        contexts_.erase(context_it);
      }
    }
  }
  // make sure env_ , env_->rendezvous_mgr available
  if (env_ == nullptr || env_->rendezvous_mgr == nullptr) {
    return tensorflow::errors::Internal(
        "invalid eager env_ or env_->rendezvous_mgr.");
  }

  auto* r = env_->rendezvous_mgr->Find(request->context_id());
  auto session_name =
      tensorflow::strings::StrCat("eager_", request->context_id());
  if (VLOG_IS_ON(2)) {
    VLOG(2) << "Creating context on /job:" << request->server_def().job_name()
            << "/task:" << request->server_def().task_index();
    for (const auto& da : request->cluster_device_attributes()) {
      VLOG(2) << "    " << da.name();
    }
  }
  TF_RETURN_IF_ERROR(env_->session_mgr->CreateSession(
      session_name, request->server_def(), request->cluster_device_attributes(),
      true));
  int64 context_id = request->context_id();
  std::function<void()> session_destroyer = [this, context_id, session_name]() {
    env_->rendezvous_mgr->Cleanup(context_id);
    auto s = env_->session_mgr->DeleteSession(session_name);
    if (!s.ok()) {
      LOG(WARNING) << "Failed to destroy worker session '" << session_name
                   << "' due to " << s.error_message();
    }
  };

  std::shared_ptr<WorkerSession> worker_session;
  TF_RETURN_IF_ERROR(env_->session_mgr->WorkerSessionForSession(
      session_name, &worker_session));

  const tensorflow::DeviceMgr* device_mgr = worker_session->device_mgr();

  // Initialize remote tensor communication based on worker session.
  TF_RETURN_IF_ERROR(r->Initialize(worker_session.get()));

  std::function<Rendezvous*(const int64)> rendezvous_creator =
      [worker_session, this](const int64 step_id) {
        auto* r = env_->rendezvous_mgr->Find(step_id);
        r->Initialize(worker_session.get()).IgnoreError();
        return r;
      };

  LOG(INFO) << "Creating " << (request->async() ? "async" : "sync")
            << " eager service context with rendezvous_id on host "
            << port::Hostname() << " " << worker_session->worker_name();
  SessionOptions opts;
  opts.config = request->server_def().default_session_config();
  tensorflow::EagerContext* ctx = new tensorflow::EagerContext(
      opts, tensorflow::ContextDevicePlacementPolicy::DEVICE_PLACEMENT_SILENT,
      request->async(), device_mgr, false, r, worker_session->cluster_flr());
  // Ownership will be transferred to the ServerContext, or else in an error
  // case ctx will be deleted by this unref.
  core::ScopedUnref unref_ctx(ctx);

  std::vector<string> remote_workers;
  worker_session->worker_cache()->ListWorkers(&remote_workers);
  remote_workers.erase(std::remove(remote_workers.begin(), remote_workers.end(),
                                   worker_session->worker_name()),
                       remote_workers.end());

  std::unique_ptr<tensorflow::eager::EagerClientCache> remote_eager_workers;
  TF_RETURN_IF_ERROR(worker_session->worker_cache()->GetEagerClientCache(
      &remote_eager_workers));
  DistributedFunctionLibraryRuntime* cluster_flr =
      eager::CreateClusterFLR(request->context_id(), ctx, worker_session.get());

  auto remote_mgr =
      absl::make_unique<tensorflow::eager::RemoteMgr>(/*is_master=*/false, ctx);
  Status s = ctx->InitializeRemoteWorker(
      std::move(remote_eager_workers), worker_session->remote_device_mgr(),
      remote_workers, request->context_id(), request->context_view_id(),
      std::move(rendezvous_creator), cluster_flr, std::move(remote_mgr),
      std::move(session_destroyer));
  if (!s.ok()) {
    VLOG(1) << "EagerContext::InitializeRemoteWorker failed with "
            << s.ToString();
    return s;
  }

  std::vector<DeviceAttributes> device_attributes;
  device_mgr->ListDeviceAttributes(&device_attributes);

  for (const auto& da : device_attributes) {
    *response->add_device_attributes() = da;
  }
  {
    mutex_lock l(contexts_mu_);
    auto context_it = contexts_.find(request->context_id());
    if (context_it != contexts_.end()) {
      return errors::InvalidArgument("EagerService:CreateContext failed. ",
                                     "Context id: <", request->context_id(),
                                     "> already exists.");
    }
    contexts_.emplace(request->context_id(),
                      new ServerContext(ctx, request->keep_alive_secs(), env_));
  }

  return Status::OK();
}

Status EagerServiceImpl::UpdateContext(const UpdateContextRequest* request,
                                       UpdateContextResponse* response) {
  // make sure env_ , env_->rendezvous_mgr available
  if (env_ == nullptr || env_->rendezvous_mgr == nullptr) {
    return tensorflow::errors::Internal(
        "invalid eager env_ or env_->rendezvous_mgr.");
  }

  // Find the context to update by the requested context_id
  ServerContext* server_context = nullptr;
  TF_RETURN_IF_ERROR(GetServerContext(request->context_id(), &server_context));
  core::ScopedUnref context_unref(server_context);

  tensorflow::EagerContext* ctx = server_context->Context();
  if (request->context_view_id() != ctx->GetContextViewId() + 1) {
    return errors::InvalidArgument(
        "EagerService:UpdateContext failed. Context id: <",
        request->context_id(), "> currently at view #", ctx->GetContextViewId(),
        " but received update request at view #", request->context_view_id(),
        ". View id should only be continuously incremented.");
  }
  if (request->cluster_device_attributes_size() == 0) {
    // In this case, the client indicates that the updated `server_def` and
    // device info is irrelevant to this worker, since it is not connected to
    // the updated ones (likely due to device filter settings). The worker
    // simply needs to update view ID and does not update other internal state.
    ctx->IncrementContextViewId();
    VLOG(1) << "Processing simplified UpdateContextRequest on "
            << ctx->HostCPU()->name();
    return Status::OK();
  }
  // TODO(b/143914772): Potential memory leak if rendezvous has pending
  // tensors for removed / replaced workers.

  auto session_name =
      tensorflow::strings::StrCat("eager_", request->context_id());

  TF_RETURN_IF_ERROR(env_->session_mgr->UpdateSession(
      session_name, request->server_def(), request->cluster_device_attributes(),
      true));

  std::shared_ptr<WorkerSession> worker_session;
  TF_RETURN_IF_ERROR(env_->session_mgr->WorkerSessionForSession(
      session_name, &worker_session));

  const tensorflow::DeviceMgr* device_mgr = worker_session->device_mgr();

  std::vector<string> remote_workers;
  worker_session->worker_cache()->ListWorkers(&remote_workers);
  remote_workers.erase(std::remove(remote_workers.begin(), remote_workers.end(),
                                   worker_session->worker_name()),
                       remote_workers.end());
  VLOG(1) << "On existing server " << worker_session->worker_name()
          << " updating remote workers";
  if (VLOG_IS_ON(2)) {
    for (const string& rw : remote_workers) {
      VLOG(2) << "Remote worker " << rw;
    }
  }

  std::unique_ptr<tensorflow::eager::EagerClientCache> remote_eager_workers;
  TF_RETURN_IF_ERROR(worker_session->worker_cache()->GetEagerClientCache(
      &remote_eager_workers));

  ctx->ClearCachesAndThreadExecutors();
  Status s = ctx->UpdateRemoteWorker(std::move(remote_eager_workers),
                                     remote_workers, request->context_id());
  if (!s.ok()) {
    VLOG(1) << "EagerContext::UpdateRemoteWorker failed with " << s.ToString();
    return s;
  }

  std::vector<DeviceAttributes> device_attributes;
  device_mgr->ListDeviceAttributes(&device_attributes);

  for (const auto& da : device_attributes) {
    *response->add_device_attributes() = da;
  }

  return Status::OK();
}

Status EagerServiceImpl::CreateMasterContext(
    const tensorflow::uint64 context_id, EagerContext* context) {
  {
    mutex_lock l(contexts_mu_);
    auto iter = contexts_.find(context_id);
    if (iter != contexts_.end()) {
      return errors::InvalidArgument(
          "EagerService:CreateMasterContext failed. ", "Context id: <",
          context_id, "> already exists.");
    }
  }
  ServerContext* server_context =
      ServerContext::CreateMasterContext(context, env_);
  mutex_lock l(contexts_mu_);
  contexts_.emplace(context_id, server_context);
  return Status::OK();
}

void EagerServiceImpl::RunComponentFunction(
    CallOptions* call_opts, const RunComponentFunctionRequest* request,
    RunComponentFunctionResponse* response, StatusCallback done) {
  ServerContext* context = nullptr;
  Status s = GetServerContext(request->context_id(), &context);
  if (!s.ok()) {
    done(s);
    return;
  }
  core::ScopedUnref context_unref(context);

  auto& operation = request->operation();
  // This codepath should only be triggered for executing component function
  if (!operation.is_function() || !operation.is_component_function()) {
    done(errors::Internal(
        "RunComponentFunction request can only be used to execute "
        "component functions."));
    return;
  }

  EagerContext* eager_context = context->Context();
  EagerExecutor* eager_executor = &eager_context->Executor();

  EagerOperation* op = new EagerOperation(eager_context);
  int* num_retvals = new int(0);
  s = GetEagerOperationAndNumRetvals(operation, eager_context, eager_executor,
                                     op, num_retvals);
  if (!s.ok()) {
    done(s);
    return;
  }
  if (!op->IsLocal()) {
    done(errors::Internal(
        "Received RunComponentFunction request with remote function device. "));
    return;
  }
  s = op->SetAttrBool("is_component_function", true);
  if (!s.ok()) {
    done(errors::Internal("Error setting is_component_function attribute: ",
                          s.error_message()));
    return;
  }

  auto* retvals = new absl::FixedArray<TensorHandle*>(*num_retvals);
  VLOG(3) << "ServerContext: Calling EagerLocalExecuteAsync for op "
          << operation.id();
  std::vector<int32> output_nums;
  for (const int32 output_num : request->output_num()) {
    output_nums.push_back(output_num);
  }

  auto cm = std::make_shared<CancellationManager>();
  op->SetCancellationManager(cm.get());
  call_opts->SetCancelCallback([cm] { cm->StartCancel(); });

  context->Ref();
  EagerLocalExecuteAsync(
      op, retvals->data(), num_retvals,
      [op, op_id = operation.id(), num_retvals, retvals, output_nums, cm,
       call_opts, response, eager_context, context,
       done = std::move(done)](const Status& status) {
        call_opts->ClearCancelCallback();
        auto wrapped_done = [&](const Status& status) {
          context->Unref();
          done(status);
          delete op;
          delete num_retvals;
          delete retvals;
        };
        if (!status.ok()) {
          wrapped_done(status);
          return;
        }
        // The output device of a component function is the component device
        // which is known on the default device of it's parent function.
        wrapped_done(AddOpRetvalsToResponse(
            eager_context, op_id, *num_retvals, output_nums, retvals->data(),
            [response] { return response->add_tensor(); },
            [response] { return response->add_shape(); }));
      });
}

Status EagerServiceImpl::ExecuteOp(CallOptions* call_opts,
                                   const Operation& operation,
                                   EagerContext* eager_context,
                                   EagerExecutor* eager_executor,
                                   QueueResponse* queue_response) {
  tensorflow::EagerOperation op(eager_context);
  int num_retvals = 0;
  TF_RETURN_IF_ERROR(GetEagerOperationAndNumRetvals(
      operation, eager_context, eager_executor, &op, &num_retvals));

  auto cm = std::make_shared<CancellationManager>();
  if (call_opts) {
    op.SetCancellationManager(cm.get());
    call_opts->SetCancelCallback([cm] { cm->StartCancel(); });
  }

  absl::FixedArray<tensorflow::TensorHandle*> retvals(num_retvals);
  VLOG(3) << "ServerContext: Calling EagerExecute for op " << operation.id();
  TF_RETURN_IF_ERROR(op.Execute(
      absl::MakeSpan(
          reinterpret_cast<tensorflow::AbstractTensorHandle**>(retvals.data()),
          num_retvals),
      &num_retvals));

  std::function<string*()> add_device_fn = nullptr;
  // Send the output devices of a function back to let a client know where the
  // outputs are. For a primitive op, an output devics is the op device which is
  // known on a client.
  if (op.is_function()) {
    add_device_fn = [queue_response] { return queue_response->add_device(); };
  }

  return AddOpRetvalsToResponse(
      eager_context, operation.id(), num_retvals, /*output_nums=*/{},
      retvals.data(), [queue_response] { return queue_response->add_tensor(); },
      [queue_response] { return queue_response->add_shape(); },
      std::move(add_device_fn));
}

Status EagerServiceImpl::Enqueue(CallOptions* call_opts,
                                 const EnqueueRequest* request,
                                 EnqueueResponse* response, uint64 stream_id) {
  profiler::TraceMe activity(
      [&] {
        return absl::StrCat(
            "EagerService:Enqueue#debug_str=", request->DebugString(), "#");
      },
      profiler::TraceMeLevel::kInfo);
  ServerContext* context = nullptr;
  TF_RETURN_IF_ERROR(GetServerContext(request->context_id(), &context));
  core::ScopedUnref context_unref(context);

  EagerExecutor& executor =
      stream_id == kInvalidStreamId
          ? context->Context()->Executor()
          : context->Context()->RemoteMgr()->GetOrCreateExecutorForStream(
                stream_id);
  Status s;
  for (const auto& item : request->queue()) {
    auto* queue_response = response->add_queue_response();
    if (item.has_operation()) {
      s = ExecuteOp(call_opts, item.operation(), context->Context(), &executor,
                    queue_response);
    } else if (item.has_handle_to_decref()) {
      auto handle_to_decref = absl::make_unique<RemoteTensorHandleInternal>(
          item.handle_to_decref());
      auto node = absl::make_unique<ClientTensorHandleDeleteNode>(
          context, std::move(handle_to_decref));
      s = context->Context()->Executor().AddOrExecute(std::move(node));
    } else if (item.has_send_tensor()) {
      s = SendTensor(item.send_tensor(), context->Context());
    } else if (item.has_send_packed_handle()) {
      s = SendPackedHandle(item.send_packed_handle(), context->Context());
    } else if (item.has_register_function()) {
      s = RegisterFunction(item.register_function(), context->Context());
    } else if (item.has_cleanup_function()) {
      s = CleanupFunction(item.cleanup_function());
    } else {
      DCHECK(item.has_sync_remote_executor_for_stream());
      s = executor.WaitForAllPendingNodes();
    }

    if (!s.ok()) {
      if (stream_id != kInvalidStreamId) {
        // TODO(b/138847548): Cleanup the executor when StreamCall is deleted.
        context->Context()->RemoteMgr()->DeleteExecutorForStream(stream_id);
      }
      return s;
    }
  }

  return Status::OK();
}

Status EagerServiceImpl::WaitQueueDone(const WaitQueueDoneRequest* request,
                                       WaitQueueDoneResponse* response) {
  ServerContext* context = nullptr;
  TF_RETURN_IF_ERROR(GetServerContext(request->context_id(), &context));
  core::ScopedUnref context_unref(context);

  if (request->op_id_size() > 0) {
    return errors::Unimplemented(
        "EagerServiceImpl::WaitQueueDone is not "
        "implemented for particular op IDs.");
  }
  return context->Context()->Executor().WaitForAllPendingNodes();
}

Status EagerServiceImpl::KeepAlive(const KeepAliveRequest* request,
                                   KeepAliveResponse* response) {
  ServerContext* context = nullptr;
  TF_RETURN_IF_ERROR(GetServerContext(request->context_id(), &context));
  core::ScopedUnref context_unref(context);

  tensorflow::EagerContext* ctx = context->Context();
  response->set_context_view_id(ctx->GetContextViewId());
  return Status::OK();
}

Status EagerServiceImpl::CloseContext(const CloseContextRequest* request,
                                      CloseContextResponse* response) {
  VLOG(1) << "Executing EagerService::CloseContext for context "
          << request->context_id();
  ServerContext* context = nullptr;
  if (!GetServerContext(request->context_id(), &context).ok()) {
    // Swallow the error here.
    return Status::OK();
  }
  core::ScopedUnref context_unref(context);

  if (request->context_view_id() < context->Context()->GetContextViewId()) {
    // Swallow the error here.
    LOG(INFO) << "Ignoring CloseContext request with a stale context_view_id "
              << request->context_view_id() << "  for context_id "
              << request->context_id() << ". The current context_view_id is "
              << context->Context()->GetContextViewId() << ".";
    return Status::OK();
  }

  mutex_lock l(contexts_mu_);
  contexts_.erase(request->context_id());

  // GetServerContext returns a newly Reffed copy of ServerContext, which is
  // unreffed by context_unref. Additionally, we need to unref it one time since
  // we are releasing it from the map.
  context->Unref();

  return Status::OK();
}

Status EagerServiceImpl::RegisterFunction(
    const RegisterFunctionOp& register_function, EagerContext* eager_context) {
  // If the function is a component of a multi-device function, we only need to
  // register it locally.
  return eager_context->AddFunctionDef(
      register_function.function_def(), register_function.library(),
      register_function.is_component_function());
}

Status EagerServiceImpl::CleanupFunction(
    const CleanupFunctionOp& cleanup_function) {
  env_->rendezvous_mgr->Cleanup(cleanup_function.step_id());
  return Status::OK();
}

Status EagerServiceImpl::SendTensor(const SendTensorOp& send_tensor,
                                    EagerContext* eager_context) {
  tensorflow::gtl::InlinedVector<tensorflow::TensorHandle*, 2> tensors;
  for (const auto& tensor_proto : send_tensor.tensors()) {
    Tensor tensor;
    if (!tensor.FromProto(tensor_proto)) {
      return errors::InvalidArgument("Unable to parse tensor proto");
    }

    TensorHandle* tensor_handle = TensorHandle::CreateLocalHandle(
        std::move(tensor), nullptr, nullptr, eager_context);
    TensorHandle* copied_handle = nullptr;
    Device* device;
    TF_RETURN_IF_ERROR(eager_context->FindDeviceFromName(
        send_tensor.device_name().c_str(), &device));
    TF_RETURN_IF_ERROR(EagerCopyToDevice(tensor_handle, eager_context,
                                         &eager_context->Executor(), device,
                                         false, &copied_handle));
    tensors.push_back(copied_handle);
    tensor_handle->Unref();
  }

  eager_context->RemoteMgr()->AddOperationOutputs(tensors, send_tensor.op_id());

  return Status::OK();
}

Status EagerServiceImpl::SendPackedHandle(
    const SendPackedHandleOp& send_packed_handle, EagerContext* eager_context) {
  if (send_packed_handle.handles().empty()) {
    return errors::InvalidArgument("Handles should not be empty.");
  }

  std::vector<tensorflow::TensorHandle*> handles;
  handles.resize(send_packed_handle.handles_size());
  for (int i = 0; i < send_packed_handle.handles_size(); ++i) {
    const auto& item = send_packed_handle.handles(i);
    if (item.has_local_handle()) {
      Tensor tensor;
      if (!ParseTensorProtoToTensor(item.local_handle().tensor(), &tensor)) {
        return errors::InvalidArgument(
            "Invalid TensorProto: ",
            item.local_handle().tensor().DebugString());
      }
      Device* op_device = nullptr;
      TF_RETURN_IF_ERROR(eager_context->FindDeviceFromName(
          item.local_handle().device().c_str(), &op_device));
      handles[i] = TensorHandle::CreateLocalHandle(
          std::move(tensor), /*d=*/nullptr, op_device, eager_context);
    } else {
      TF_RETURN_IF_ERROR(
          eager_context->RemoteMgr()->DeserializeRemoteTensorHandle(
              item.remote_handle(), &handles[i]));
    }
  }

  tensorflow::TensorHandle* packed_handle = nullptr;
  std::vector<tensorflow::TensorHandle*> handles_to_pack = handles;
  // Create a unshaped packed TensorHandle.
  TF_RETURN_IF_ERROR(TensorHandle::CreatePackedHandle(
      std::move(handles_to_pack), handles.at(0)->dtype, TensorShape(),
      send_packed_handle.device_name(), eager_context, &packed_handle));

  for (auto* h : handles) {
    // Unref handle since it has a ref in the packed handle now.
    h->Unref();
  }

  eager_context->RemoteMgr()->AddOperationOutputs({packed_handle},
                                                  send_packed_handle.op_id());
  return Status::OK();
}

tensorflow::Status EagerServiceImpl::GetServerContext(
    uint64 context_id, ServerContext** server_context) {
  tf_shared_lock l(contexts_mu_);
  auto iter = contexts_.find(context_id);
  if (iter == contexts_.end()) {
    *server_context = nullptr;
    return errors::Unavailable(strings::Printf(
        "Unable to find a context_id matching the specified one "
        "(%llu). Perhaps the worker was restarted, or the context was GC'd?",
        static_cast<unsigned long long>(context_id)));
  }

  *server_context = iter->second;
  (*server_context)->Ref();

  (*server_context)->RecordAccess();

  return Status::OK();
}

}  // namespace eager
}  // namespace tensorflow