android-11.0.0_r48/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/c/c_api_experimental.h"

#include "absl/strings/substitute.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/checkpoint_reader.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_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"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/net.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

void TF_EnableXLACompilation(TF_SessionOptions* options, unsigned char enable) {
  tensorflow::ConfigProto& config = options->options.config;
  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }
}

unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) {
  tensorflow::BuildXlaOpsPassFlags* flags =
      tensorflow::GetBuildXlaOpsPassFlags();
  bool original = flags->tf_xla_enable_lazy_compilation;
  flags->tf_xla_enable_lazy_compilation = enable;
  return original;
}

unsigned char TF_SetTfXlaCpuGlobalJit(unsigned char enable) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  bool original = flags->tf_xla_cpu_global_jit;
  flags->tf_xla_cpu_global_jit = static_cast<bool>(enable);
  return static_cast<unsigned char>(original);
}

void TF_SetXlaAutoJitMode(const char* mode) {
  tensorflow::SetXlaAutoJitFlagFromFlagString(mode);
}

unsigned char TF_GetXlaConstantFoldingDisabled() {
  return static_cast<unsigned char>(
      tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding);
}

void TF_SetXlaConstantFoldingDisabled(unsigned char should_enable) {
  tensorflow::GetBuildXlaOpsPassFlags()->tf_xla_disable_constant_folding =
      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  flags->tf_xla_min_cluster_size = size;
}

TF_Buffer* TF_CreateConfig(unsigned char enable_xla_compilation,
                           unsigned char gpu_memory_allow_growth,
                           unsigned int num_cpu_devices) {
  tensorflow::ConfigProto config;
  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable_xla_compilation) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph
  // functions, and their send/recvs corountines).
  config.set_inter_op_parallelism_threads(1);

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {
    options.set_trace_level(tensorflow::RunOptions::FULL_TRACE);
  } else {
    options.set_trace_level(tensorflow::RunOptions::NO_TRACE);
  }
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(options, ret));
  return ret;
}

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->fdef);
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,
    std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {
  tensorflow::GraphDef gdef;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);
    return {};
  }
  std::vector<UniqueFuncPtr> ret;
  for (const FunctionDef& fdef : fdef_lib.function()) {
    // Make a copy so that we can mutate it.
    FunctionDef fdef_to_load = fdef;
    if (mutate_proto_func) {
      (*mutate_proto_func)(&fdef_to_load);
    }
    VLOG(1) << "Adding func to graph: " << fdef_to_load.DebugString();
    std::vector<char> binary_proto_buf(fdef_to_load.ByteSizeLong());
    fdef_to_load.SerializeToArray(binary_proto_buf.data(),
                                  binary_proto_buf.size());
    TF_Function* func = TF_FunctionImportFunctionDef(
        binary_proto_buf.data(), binary_proto_buf.size(), status);
    if (!status->status.ok()) return {};
    ret.push_back(UniqueFuncPtr(func, TF_DeleteFunction));
  }
  return ret;
}

TF_Tensor* TF_DequeueNamedTensor(TF_Session* session, int tensor_id,
                                 TF_Status* status) {
  assert(session);
  {
    tensorflow::mutex_lock c(session->graph->mu);
    VLOG(1) << "Dequeuing named tensor with id " << tensor_id
            << ", with input graph: "
            << session->graph->graph.ToGraphDefDebug().DebugString();
  }

  TF_Operation* dequeue_op = TF_GraphOperationByName(
      session->graph,
      tensorflow::strings::StrCat("fifo_queue_dequeue_", tensor_id).c_str());
  if (dequeue_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the dequeue node in the TF graph.");
    return nullptr;
  }

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,
                /*run_metadata*/ nullptr, status);
  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;
    if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) {
      VLOG(1) << "Dequeued tensor content: " << tensor.DebugString();
    }
  }
  return ret;
}

void TF_EnqueueNamedTensor(TF_Session* session, int tensor_id,
                           TF_Tensor* tensor, TF_Status* status) {
  assert(session);
  {
    tensorflow::mutex_lock c(session->graph->mu);
    if (VLOG_IS_ON(1)) {
      VLOG(1) << "Enqueuing named tensor with id " << tensor_id
              << ", with input graph: "
              << session->graph->graph.ToGraphDefDebug().DebugString();
      tensorflow::Tensor internal_tensor;
      if (tensorflow::TF_TensorToTensor(tensor, &internal_tensor).ok()) {
        VLOG(1) << "Enqueu'ing tensor content: "
                << internal_tensor.DebugString();
      }
    }
  }

  TF_Operation* enqueue_op = TF_GraphOperationByName(
      session->graph,
      tensorflow::strings::StrCat("fifo_queue_enqueue_", tensor_id).c_str());
  if (enqueue_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the enqueue node in the TF graph.");
    return;
  }

  TF_Operation* placeholder_op = TF_GraphOperationByName(
      session->graph,
      tensorflow::strings::StrCat("arg_tensor_enqueue_", tensor_id).c_str());
  if (placeholder_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ &input, /*input_values*/ &tensor, /*ninputs*/ 1,
                // output related parameters
                /*outputs*/ nullptr, /*output_values*/ nullptr, /*noutputs*/ 0,
                /*targets*/ &enqueue_op, /*ntargets*/ 1,
                /*run_metadata*/ nullptr, status);
  VLOG(1) << "Enqueuing is done.";
}

TF_Buffer* TFE_GetServerDef(const char* text_proto, TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto,
                                                         &server_def)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for ServerDef: ", text_proto);
    return nullptr;
  }
  status->status = tensorflow::Status();
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(server_def, ret));
  return ret;
}

TFE_Context* TFE_CreateContextFromSession(TF_Session* session,
                                          TF_Status* status) {
  auto* opts = TFE_NewContextOptions();

  // Reduce GPU memory allocation, and set appropriate config options for TFE
  // context.
  auto* config = TF_CreateConfig(
      /*xla*/ false, /* gpu_memory_allow_growth */ true, /* num_cpu_devices */
      10);
  TFE_ContextOptionsSetConfig(opts, config->data, config->length, status);
  if (!status->status.ok()) {
    CHECK(!config);
    TFE_DeleteContextOptions(opts);
    return nullptr;
  }

  auto* ctx = TFE_NewContextFromSession(opts, session, status);
  TF_DeleteBuffer(config);
  TFE_DeleteContextOptions(opts);
  return ctx;
}

// TODO: retrieve the device string via TFE_ContextListDevices()
static const char DEFAULT_CPU_DEVICE[] =
    "/job:localhost/replica:0/task:0/device:CPU:0";

static TFE_TensorHandle* createTFEQueue(TFE_Context* ctx, TF_DataType inputType,
                                        int tensor_id, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> queueOp(
      TFE_NewOp(ctx, "FIFOQueueV2", status), TFE_DeleteOp);
  TFE_OpSetDevice(queueOp.get(), DEFAULT_CPU_DEVICE, status);
  if (!status->status.ok()) return nullptr;
  // TODO: use NAMED_TENSOR_QUEUE_CAPACITY in S4TF compiler.
  TFE_OpSetAttrInt(queueOp.get(), "capacity", 1);
  TFE_OpSetAttrTypeList(queueOp.get(), "component_types", &inputType, 1);
  auto shared_name = tensorflow::strings::StrCat("fifo_queue_", tensor_id);
  TFE_OpSetAttrString(queueOp.get(), "shared_name", shared_name.data(),
                      shared_name.size());
  TFE_OpSetAttrString(queueOp.get(), "container", "", 0);

  // TODO: consider making this an unknown shape.
  const int64_t* dims_ptr = nullptr;
  int num_dims = 0;
  TFE_OpSetAttrShapeList(queueOp.get(), "shapes", &dims_ptr, &num_dims,
                         /*num_values*/ 0, status);
  if (!status->status.ok()) return nullptr;

  int num_retvals = 1;
  TFE_TensorHandle* queue = nullptr;
  TFE_Execute(queueOp.get(), &queue, &num_retvals, status);
  if (!status->status.ok()) return nullptr;
  CHECK_EQ(num_retvals, 1);

  return queue;
}

static void createTFEEnqueue(TFE_Context* ctx, TF_DataType inputType,
                             TFE_TensorHandle* queue, TFE_TensorHandle* tensor,
                             TF_Status* status) {
  TFE_Op* op = TFE_NewOp(ctx, "QueueEnqueueV2", status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op_deleter(op, TFE_DeleteOp);
  TFE_OpSetDevice(op, DEFAULT_CPU_DEVICE, status);
  if (!status->status.ok()) return;
  TFE_OpAddInput(op, queue, status);
  if (!status->status.ok()) return;
  TFE_OpAddInput(op, tensor, status);
  if (!status->status.ok()) return;
  TFE_OpSetAttrTypeList(op, "Tcomponents", &inputType, 1);
  TFE_OpSetAttrInt(op, "timeout_ms", -1);

  int num_retvals = 0;
  TFE_Execute(op, nullptr /*retvals*/, &num_retvals, status);
  if (!status->status.ok()) return;
  CHECK_EQ(num_retvals, 0);
}

static TFE_TensorHandle* createTFEDequeue(TFE_Context* ctx,
                                          TF_DataType inputType,
                                          TFE_TensorHandle* queue,
                                          TF_Status* status) {
  TFE_Op* op = TFE_NewOp(ctx, "QueueDequeueV2", status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op_deleter(op, TFE_DeleteOp);
  TFE_OpSetDevice(op, DEFAULT_CPU_DEVICE, status);
  if (!status->status.ok()) return nullptr;

  TFE_OpAddInput(op, queue, status);
  if (!status->status.ok()) return nullptr;
  TFE_OpSetAttrTypeList(op, "component_types", &inputType, 1);
  TFE_OpSetAttrInt(op, "timeout_ms", -1);
  TFE_TensorHandle* ret;
  int num_retvals = 1;
  TFE_Execute(op, &ret, &num_retvals, status);
  if (!status->status.ok()) return nullptr;
  CHECK_EQ(num_retvals, 1);
  return ret;
}

TFE_TensorHandle* TFE_DequeueNamedTensor(TF_Session* session, int tensor_id,
                                         TF_DataType inputType,
                                         TF_Status* status) {
  assert(session);
  VLOG(1) << "Dequeuing data tensor with id " << tensor_id;

  auto ctx = TFE_CreateContextFromSession(session, status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> ctx_deleter(
      ctx, TFE_DeleteContext);

  TFE_TensorHandle* queue = createTFEQueue(ctx, inputType, tensor_id, status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  auto* ret = createTFEDequeue(ctx, inputType, queue, status);
  return ret;
}

TFE_TensorHandle* TFE_DequeueNamedTensorFromCtx(TFE_Context* ctx, int tensor_id,
                                                TF_DataType inputType,
                                                TF_Status* status) {
  TFE_TensorHandle* queue = createTFEQueue(ctx, inputType, tensor_id, status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  auto* ret = createTFEDequeue(ctx, inputType, queue, status);

  return ret;
}

void TFE_EnqueueNamedTensor(TF_Session* session, int tensor_id,
                            TFE_TensorHandle* tensor, TF_Status* status) {
  assert(session);
  VLOG(1) << "Enqueuing data tensor with id " << tensor_id;

  auto ctx = TFE_CreateContextFromSession(session, status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> ctx_deleter(
      ctx, TFE_DeleteContext);

  TF_DataType inputType = TFE_TensorHandleDataType(tensor);
  TFE_TensorHandle* queue = createTFEQueue(ctx, inputType, tensor_id, status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  createTFEEnqueue(ctx, inputType, queue, tensor, status);
}

void TFE_EnqueueNamedTensorFromCtx(TFE_Context* ctx, int tensor_id,
                                   TFE_TensorHandle* tensor,
                                   TF_Status* status) {
  VLOG(1) << "Enqueuing data tensor with id " << tensor_id;

  TF_DataType inputType = TFE_TensorHandleDataType(tensor);
  TFE_TensorHandle* queue = createTFEQueue(ctx, inputType, tensor_id, status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  createTFEEnqueue(ctx, inputType, queue, tensor, status);
}

void TFE_EnqueueVariantTensor(TF_Session* session, int tensor_id,
                              TFE_TensorHandle* tensor, TF_Status* status) {
  VLOG(1) << "Enqueuing variant tensor with id " << tensor_id;

  auto ctx = TFE_CreateContextFromSession(session, status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> ctx_deleter(
      ctx, TFE_DeleteContext);

  TFE_TensorHandle* queue = createTFEQueue(ctx, TF_VARIANT, tensor_id, status);
  if (!status->status.ok()) return;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  createTFEEnqueue(ctx, TF_VARIANT, queue, tensor, status);
}

TFE_TensorHandle* TFE_DequeueVariantTensor(TF_Session* session, int tensor_id,
                                           TF_Status* status) {
  VLOG(1) << "Dequeuing variant tensor with id " << tensor_id;

  auto ctx = TFE_CreateContextFromSession(session, status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_Context, decltype(&TFE_DeleteContext)> ctx_deleter(
      ctx, TFE_DeleteContext);

  TFE_TensorHandle* queue = createTFEQueue(ctx, TF_VARIANT, tensor_id, status);
  if (!status->status.ok()) return nullptr;
  std::unique_ptr<TFE_TensorHandle, decltype(&TFE_DeleteTensorHandle)>
      queue_deleter(queue, TFE_DeleteTensorHandle);

  return createTFEDequeue(ctx, TF_VARIANT, queue, status);
}

void TFE_TensorHandlePrintDebugString(TFE_TensorHandle* handle) {
  auto* status = TF_NewStatus();
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  tensorflow::Tensor dst;
  TF_CHECK_OK(TF_TensorToTensor(t, &dst));
  LOG(INFO) << dst.DebugString();

  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
}

void TFE_OpPrintDebugString(TFE_Op* op) {
  VLOG(1) << "TFE_OpPrintDebugString() over " << op;
  LOG(INFO) << op->operation.DebugString();
}

struct TFE_ExecuteOpNotification {
  TFE_ExecuteOpNotification() : status(TF_NewStatus(), TF_DeleteStatus) {}
  tensorflow::Notification n;
  std::unique_ptr<tensorflow::Thread> thread;
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status;
};

TFE_ExecuteOpNotification* TFE_ExecuteOpInNewThread(TFE_Op* op,
                                                    TFE_TensorHandle** retvals,
                                                    int* num_retvals,
                                                    TF_Status* status) {
  TFE_ExecuteOpNotification* n = new TFE_ExecuteOpNotification;

  n->thread.reset(op->operation.EagerContext().TFEnv()->StartThread(
      tensorflow::ThreadOptions(), "ExecuteOpThread",
      [op, retvals, num_retvals, n]() {
        TFE_Execute(op, retvals, num_retvals, n->status.get());
        n->n.Notify();
      }));

  return n;
}

void TFE_ExecuteOpNotificationWaitAndDelete(
    TFE_ExecuteOpNotification* notification, TF_Status* status) {
  if (notification == nullptr) {
    status->status = tensorflow::errors::InvalidArgument(
        "Passed in notification is a nullptr.");

    return;
  }
  if (notification->thread == nullptr) {
    status->status = tensorflow::errors::InvalidArgument(
        "Passed in notification didn't start a thread correctly. Cleaning up "
        "this notification. Please re-execute the operation to get a new "
        "notification.");

    delete notification;
    return;
  }

  notification->n.WaitForNotification();

  status->status = notification->status->status;

  delete notification;
}

void TF_MakeInternalErrorStatus(TF_Status* status, const char* errMsg) {
  status->status = tensorflow::errors::Internal(errMsg);
}

struct TF_CheckpointReader : public tensorflow::checkpoint::CheckpointReader {
  using tensorflow::checkpoint::CheckpointReader::CheckpointReader;
  std::vector<std::string> variable_list;
};

TF_CheckpointReader* TF_NewCheckpointReader(const char* filename,
                                            TF_Status* status) {
  TF_CheckpointReader* reader = new TF_CheckpointReader(filename, status);
  if (!status->status.ok()) {
    TF_DeleteCheckpointReader(reader);
    return nullptr;
  }
  const auto& m = reader->GetVariableToDataTypeMap();
  for (auto it = m.begin(); it != m.end(); ++it)
    reader->variable_list.push_back(it->first);
  std::sort(reader->variable_list.begin(), reader->variable_list.end());
  return reader;
}

void TF_DeleteCheckpointReader(TF_CheckpointReader* reader) { delete reader; }

int TF_CheckpointReaderHasTensor(TF_CheckpointReader* reader,
                                 const char* name) {
  return reader->HasTensor(name);
}

const char* TF_CheckpointReaderGetVariable(TF_CheckpointReader* reader,
                                           int index) {
  return reader->variable_list[index].c_str();
}

int TF_CheckpointReaderSize(TF_CheckpointReader* reader) {
  return reader->variable_list.size();
}

TF_DataType TF_CheckpointReaderGetVariableDataType(TF_CheckpointReader* reader,
                                                   const char* name) {
  const auto& m = reader->GetVariableToDataTypeMap();
  return static_cast<TF_DataType>(m.at(name));
}

TF_Tensor* TF_CheckpointReaderGetTensor(TF_CheckpointReader* reader,
                                        const char* name, TF_Status* status) {
  std::unique_ptr<tensorflow::Tensor> tensor;
  reader->GetTensor(name, &tensor, status);
  if (!status->status.ok()) return nullptr;
  return tensorflow::TF_TensorFromTensor(*tensor, &status->status);
}

void TF_CheckpointReaderGetVariableShape(TF_CheckpointReader* reader,
                                         const char* name, int64_t* dims,
                                         int num_dims, TF_Status* status) {
  const auto& shape = reader->GetVariableToShapeMap().at(name);
  int rank = shape.dims();
  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }
  for (int i = 0; i < num_dims; i++) {
    dims[i] = shape.dim_size(i);
  }
}

int TF_CheckpointReaderGetVariableNumDims(TF_CheckpointReader* reader,
                                          const char* name) {
  const auto& m = reader->GetVariableToShapeMap();
  return m.at(name).dims();
}

// This builder is used in the eager API to build a NodeDef.
struct TF_AttrBuilder : public tensorflow::AttrBuilder {
  using tensorflow::AttrBuilder::AttrBuilder;
  // The string buffers to make sure that any `attr_name` we pass into
  // `builder->Set()` will outlive the subsequent
  // `TF_AttrBuilderCheckCanRunOnDevice()` call(s) on the same `builder`.
  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(
      (*iter).c_str(),
      tensorflow::gtl::ArraySlice<const tensorflow::DataType>(
          reinterpret_cast<const tensorflow::DataType*>(values), num_values));
}

void TF_AttrBuilderCheckCanRunOnDevice(TF_AttrBuilder* builder,
                                       const char* device_type,
                                       TF_Status* status) {
  status->status = tensorflow::FindKernelDef(
      tensorflow::DeviceType(device_type), builder->BuildNodeDef(),
      /* def = */ nullptr, /* kernel_class_name = */ nullptr);
}

const char* TF_GetNumberAttrForOpListInput(const char* op_name, int input_index,
                                           TF_Status* status) {
  const tensorflow::OpDef* op_def = nullptr;
  status->status =
      tensorflow::OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(
        input_index, " out of range for ", op_name);
    return nullptr;
  }

  const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index];

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(
        op_name, " does not have number_attr() defined.");
    return nullptr;
  }

  // The returned string is owned by OpRegistry, so liveness is not a concern.
  return input_arg.number_attr().c_str();
}

int TF_OpIsStateful(const char* op_type, TF_Status* status) {
  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =
      tensorflow::OpRegistry::Global()->LookUp(op_type, &op_reg_data);
  if (!status->status.ok()) {
    return 0;
  }
  return op_reg_data->op_def.is_stateful();
}

void TF_InitMain(const char* usage, int* argc, char*** argv) {
  tensorflow::port::InitMain(usage, argc, argv);
}

int TF_PickUnusedPortOrDie() {
  return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);
  return TFE_TensorHandle::CreateLocalHandle(tensor, status);
}

namespace {
tensorflow::Status EnableCollectiveOps(const tensorflow::ServerDef& server_def,
                                       TFE_Context* ctx) {
  // We don't use the TF_RETURN_IF_ERROR macro directly since that destroys the
  // server object (which currently CHECK-fails) and we miss the error, instead,
  // we log the error, and then return to allow the user to see the error
  // message.
#define LOG_AND_RETURN_IF_ERROR(...)                    \
  do {                                                  \
    const ::tensorflow::Status _status = (__VA_ARGS__); \
    if (TF_PREDICT_FALSE(!_status.ok())) {              \
      LOG(ERROR) << _status.error_message();            \
      return _status;                                   \
    }                                                   \
  } while (0);

  // New server created for new server_def. Unused if updating server_def.
  tensorflow::EagerContext* context = ctx->context;
  tensorflow::GrpcServer* grpc_server =
      dynamic_cast<tensorflow::GrpcServer*>(context->GetServer());
  if (grpc_server == nullptr) {
    std::unique_ptr<tensorflow::ServerInterface> new_server;
    LOG_AND_RETURN_IF_ERROR(tensorflow::NewServer(server_def, &new_server));
    grpc_server = dynamic_cast<tensorflow::GrpcServer*>(new_server.get());
    if (grpc_server == nullptr) {
      LOG_AND_RETURN_IF_ERROR(tensorflow::errors::Internal(
          "Currently, TFE_NewContext only supports tensorflow::GrpcServer."));
    }
    LOG_AND_RETURN_IF_ERROR(grpc_server->Start());

    LOG_AND_RETURN_IF_ERROR(context->StoreCollectiveOpsServer(
        std::move(new_server), grpc_server->worker_env()->device_mgr,
        grpc_server->worker_env()->collective_executor_mgr));
  } else {
    LOG_AND_RETURN_IF_ERROR(grpc_server->UpdateServerDef(server_def));
    LOG_AND_RETURN_IF_ERROR(context->StoreCollectiveOpsServer(
        /*new_server=*/nullptr, grpc_server->worker_env()->device_mgr,
        grpc_server->worker_env()->collective_executor_mgr));
  }
  return tensorflow::Status::OK();
#undef LOG_AND_RETURN_IF_ERROR
}
}  // namespace

// Set server_def on the context, possibly updating it.
TF_CAPI_EXPORT extern void TFE_EnableCollectiveOps(TFE_Context* ctx,
                                                   const void* proto,
                                                   size_t proto_len,
                                                   TF_Status* status) {
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }
  status->status = EnableCollectiveOps(server_def, ctx);
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {
  TF_ShapeAndTypeList* result = new TF_ShapeAndTypeList;
  result->num_items = num_items;
  result->items = (num_items == 0) ? nullptr : new TF_ShapeAndType[num_items]();
  return result;
}

void TF_ShapeAndTypeListSetShape(TF_ShapeAndTypeList* shape_list, int index,
                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);
}

void TF_ShapeAndTypeListSetUnknownShape(TF_ShapeAndTypeList* shape_list,
                                        int index) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  shape.num_dims = -1;
  shape.dims = nullptr;
}

void TF_ShapeAndTypeListSetDtype(TF_ShapeAndTypeList* shape_list, int index,
                                 TF_DataType dtype) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape_and_type = shape_list->items[index];
  shape_and_type.dtype = dtype;
}

void TF_DeleteShapeAndTypeList(TF_ShapeAndTypeList* shape_list) {
  if (shape_list == nullptr) return;
  for (size_t i = 0; i < shape_list->num_items; ++i) {
    delete[] shape_list->items[i].dims;
  }
  delete[] shape_list->items;
  delete shape_list;
}

void TF_DeleteShapeAndTypeListArray(TF_ShapeAndTypeList** shape_list_array,
                                    int num_items) {
  if (shape_list_array == nullptr) return;
  for (int i = 0; i < num_items; ++i) {
    TF_DeleteShapeAndTypeList(shape_list_array[i]);
  }
  delete[] shape_list_array;
}

namespace tensorflow {
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);
}  // namespace tensorflow

void TFE_InferShapes(TFE_Op* tfe_op, TF_ShapeAndTypeList* input_shapes,
                     TF_Tensor** input_tensors,
                     TF_ShapeAndTypeList* input_tensors_as_shapes,
                     TF_ShapeAndTypeList** input_resource_shapes_and_types,
                     TF_ShapeAndTypeList** output_shapes,
                     TF_ShapeAndTypeList*** output_resource_shapes_and_types,
                     TF_Status* status) {
  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;
  using tensorflow::shape_inference::DimensionHandle;
  using tensorflow::shape_inference::InferenceContext;
  using tensorflow::shape_inference::ShapeAndType;
  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;
  node_def.set_name(tfe_op->operation.Name());
  node_def.set_op(tfe_op->operation.Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  tfe_op->operation.Attrs().FillAttrValueMap(node_def.mutable_attr());

  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =
      tensorflow::OpRegistry::Global()->LookUp(node_def.op(), &op_reg_data);
  if (!status->status.ok()) return;

  // Initialize a input_tensor vector with `nullptr` values.
  std::vector<const Tensor*> input_tensors_vector(num_inputs, nullptr);
  // A vector to keep track of newly created `tf::Tensor` objects.
  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {
    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);
    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,
                     {},
                     std::vector<std::unique_ptr<std::vector<ShapeAndType>>>());

  // Set input_shapes.
  for (int i = 0; i < num_inputs; ++i) {
    std::vector<DimensionHandle> dims;
    const TF_ShapeAndType& input_shape = input_shapes->items[i];
    if (input_shape.num_dims == InferenceContext::kUnknownRank) {
      c.SetInput(i, c.UnknownShape());
      continue;
    }
    for (int j = 0; j < input_shape.num_dims; ++j) {
      dims.push_back(c.MakeDim(input_shape.dims[j]));
    }
    c.SetInput(i, c.MakeShape(dims));
  }

  // TODO(bgogul): Handle input_tensors_as_shapes.
  // TODO(bgogul): Handle input_resource_shapes_and_types.

  status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);
  if (!status->status.ok()) return;

  // Set output_shapes.
  TF_ShapeAndTypeList* output_shapes_result =
      TF_NewShapeAndTypeList(c.num_outputs());
  for (int i = 0; i < c.num_outputs(); ++i) {
    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));
    }
  }
  if (output_shapes != nullptr) *output_shapes = output_shapes_result;

  // TODO(bgogul): Set output_resource_shapes_and_types.
}

void TF_ImportGraphDefOptionsSetValidateColocationConstraints(
    TF_ImportGraphDefOptions* opts, unsigned char enable) {
  opts->opts.validate_colocation_constraints = enable;
}