/* Copyright 2017 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/compiler/jit/kernels/xla_ops.h"

#include "absl/container/flat_hash_map.h"
#include "absl/memory/memory.h"
#include "tensorflow/compiler/jit/defs.h"
#include "tensorflow/compiler/jit/flags.h"
#include "tensorflow/compiler/jit/xla_activity_listener.h"
#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/client/client_library.h"
#include "tensorflow/compiler/xla/client/local_client.h"
#include "tensorflow/compiler/xla/service/compiler.h"
#include "tensorflow/compiler/xla/status_macros.h"
#include "tensorflow/compiler/xla/statusor.h"
#include "tensorflow/core/common_runtime/dma_helper.h"
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/stream_executor_no_cuda.h"
#include "tensorflow/core/profiler/lib/traceme.h"
#include "tensorflow/core/util/stream_executor_util.h"

// OP_REQUIRES_OK_RETURN is the same as OP_REQUIRES_OK except that
// in error case, it returns RET instead of void.
#define OP_REQUIRES_OK_RETURN(CTX, RET, ...)                \
  do {                                                      \
    ::tensorflow::Status _s(__VA_ARGS__);                   \
    if (!TF_PREDICT_TRUE(_s.ok())) {                        \
      (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \
      return RET;                                           \
    }                                                       \
  } while (0)

namespace tensorflow {

namespace {

XlaPlatformInfo PlatformInfoFromContext(OpKernelConstruction* ctx) {
  DeviceType device_type = ctx->device_type();
  se::Platform::Id platform_id = nullptr;
  const XlaDevice::Metadata* xla_device_metadata = nullptr;
  se::DeviceMemoryAllocator* custom_allocator = nullptr;

  if (ctx->device_type() == DeviceType(DEVICE_CPU)) {
    platform_id = se::host::kHostPlatformId;
  } else if (ctx->device_type() == DeviceType(DEVICE_GPU)) {
    platform_id = ctx->device()
                      ->tensorflow_gpu_device_info()
                      ->stream->parent()
                      ->platform()
                      ->id();
  } else if (XlaDevice::GetMetadata(ctx, &xla_device_metadata).ok()) {
    // If we are on an XlaDevice, use the underlying XLA platform's allocator
    // directly. We could use the StreamExecutor's allocator which may
    // theoretically be more correct, but XLA returns a nice OOM message in a
    // Status and StreamExecutor does not.
    //
    // Importantly we can't use ctx->device()->GetAllocator() as the allocator
    // (which xla_allocator above uses) as on an XlaDevice, this is a dummy
    // allocator that returns XlaTensor objects. The XlaCompiler needs a real
    // allocator to allocate real buffers.
    platform_id = xla_device_metadata->platform()->id();
    custom_allocator =
        xla_device_metadata->client()->backend().memory_allocator();
  }

  return XlaPlatformInfo(device_type, platform_id, xla_device_metadata,
                         custom_allocator);
}

// A closure describing how to run a compiled version of a TensorFlow function.
//
// It may seem unusual to stick the resource variable snapshots in this class.
// This is necessary: we need to use the snapshots observed by the compiler as
// the initial values for the resource variables (and cannot snapshot them again
// during execution) because otherwise we risk observing a different snapshot
// with shapes different from what we compiled for.
class XlaExecutableClosure {
 public:
  explicit XlaExecutableClosure(
      xla::LocalClient* client, xla::LocalExecutable* executable,
      const XlaCompiler::CompilationResult* compilation_result,
      std::map<int, OptionalTensor> resource_var_snapshots,
      int num_constant_args)
      : client_(client),
        executable_(executable),
        compilation_result_(compilation_result),
        resource_var_snapshots_(std::move(resource_var_snapshots)),
        num_constant_args_(num_constant_args) {}

  XlaExecutableClosure(XlaExecutableClosure&&) = default;
  XlaExecutableClosure& operator=(XlaExecutableClosure&&) = default;

  xla::LocalClient* client() const { return client_; }
  xla::LocalExecutable* executable() const { return executable_; }
  const XlaCompiler::CompilationResult* compilation_result() const {
    return compilation_result_;
  }
  const std::map<int, OptionalTensor>& resource_var_snapshots() const {
    return resource_var_snapshots_;
  }
  int num_constant_args() const { return num_constant_args_; }

 private:
  xla::LocalClient* client_;
  xla::LocalExecutable* executable_;
  const XlaCompiler::CompilationResult* compilation_result_;
  std::map<int, OptionalTensor> resource_var_snapshots_;
  int num_constant_args_;

  TF_DISALLOW_COPY_AND_ASSIGN(XlaExecutableClosure);
};

// This maintains a mapping from a globally unique ID to XlaExecutableClosure
// instances.
class XlaExecutableClosureStore {
 public:
  XlaExecutableClosureStore() : key_counter_(0) {}

  using KeyT = string;

  KeyT Produce(XlaExecutableClosure result) {
    mutex_lock l(mutex_);
    KeyT key = absl::StrCat(key_counter_++);
    bool insert_successful = closures_.emplace(key, std::move(result)).second;
    DCHECK(insert_successful);
    (void)insert_successful;
    return key;
  }

  XlaExecutableClosure Consume(const KeyT& key) {
    mutex_lock l(mutex_);
    auto it = closures_.find(key);
    DCHECK(it != closures_.end());
    XlaExecutableClosure value = std::move(it->second);
    closures_.erase(it);
    return value;
  }

  static XlaExecutableClosureStore* Global() {
    static XlaExecutableClosureStore* instance = new XlaExecutableClosureStore;
    return instance;
  }

 private:
  mutex mutex_;
  int64 key_counter_ GUARDED_BY(mutex_);
  absl::flat_hash_map<KeyT, XlaExecutableClosure> closures_ GUARDED_BY(mutex_);

  TF_DISALLOW_COPY_AND_ASSIGN(XlaExecutableClosureStore);
};

// Return allocator from platform info if non-null, or populate and return a
// pointer to the allocator adapter with allocator from context.
//
// This is necessary because for XLA devices the underlying TF allocator returns
// dummy tensors.
se::DeviceMemoryAllocator* GetAllocator(
    absl::optional<se::TfAllocatorAdapter>* tf_allocator_adapter,
    OpKernelContext* ctx, const XlaPlatformInfo& platform_info) {
  if (platform_info.custom_allocator()) {
    return platform_info.custom_allocator();
  }
  if (!ctx->op_device_context()) {
    // Stream is not set for the host platform.
    se::Platform* platform =
        se::MultiPlatformManager::PlatformWithId(platform_info.platform_id())
            .ValueOrDie();
    tf_allocator_adapter->emplace(ctx->device()->GetAllocator({}), platform);
    return &tf_allocator_adapter->value();
  }
  // platform_info.
  tf_allocator_adapter->emplace(ctx->device()->GetAllocator({}),
                                ctx->op_device_context()->stream());
  return &tf_allocator_adapter->value();
}

}  // namespace

XlaLocalLaunchBase::XlaLocalLaunchBase(OpKernelConstruction* ctx,
                                       const std::vector<int>& constants,
                                       const std::vector<int>& resources,
                                       const NameAttrList& function)
    : OpKernel(ctx),
      constants_(constants),
      resources_(resources),
      function_(function),
      platform_info_(PlatformInfoFromContext(ctx)) {}

static Status BuildCompilationCache(OpKernelContext* ctx,
                                    const XlaPlatformInfo& platform_info,
                                    XlaCompilationCache** cache) {
  if (platform_info.xla_device_metadata()) {
    *cache = new XlaCompilationCache(
        platform_info.xla_device_metadata()->client(),
        platform_info.xla_device_metadata()->jit_device_type());
    return Status::OK();
  }

  auto platform =
      se::MultiPlatformManager::PlatformWithId(platform_info.platform_id());
  if (!platform.ok()) {
    return platform.status();
  }

  xla::StatusOr<xla::Compiler*> compiler_for_platform =
      xla::Compiler::GetForPlatform(platform.ValueOrDie());
  if (!compiler_for_platform.ok()) {
    // In some rare cases (usually in unit tests with very small clusters) we
    // may end up transforming an XLA cluster with at least one GPU operation
    // (which would normally force the cluster to be compiled using XLA:GPU)
    // into an XLA cluster with no GPU operations (i.e. containing only CPU
    // operations).  Such a cluster can fail compilation (in way that
    // MarkForCompilation could not have detected) if the CPU JIT is not linked
    // in.
    //
    // So bail out of _XlaCompile in this case, and let the executor handle the
    // situation for us.
    const Status& status = compiler_for_platform.status();
    if (status.code() == error::NOT_FOUND) {
      return errors::Unimplemented("Could not find compiler for platform ",
                                   platform.ValueOrDie()->Name(), ": ",
                                   status.ToString());
    }
  }

  xla::LocalClientOptions client_options;
  client_options.set_platform(platform.ValueOrDie());
  client_options.set_intra_op_parallelism_threads(
      ctx->device()->tensorflow_cpu_worker_threads()->num_threads);
  auto client = xla::ClientLibrary::GetOrCreateLocalClient(client_options);
  if (!client.ok()) {
    return client.status();
  }
  const XlaOpRegistry::DeviceRegistration* registration;
  if (!XlaOpRegistry::GetCompilationDevice(platform_info.device_type().type(),
                                           &registration)) {
    return errors::InvalidArgument("No JIT device registered for ",
                                   platform_info.device_type().type());
  }
  *cache = new XlaCompilationCache(
      client.ValueOrDie(), DeviceType(registration->compilation_device_name));
  return Status::OK();
}

static Status CompileToLocalExecutable(
    OpKernelContext* ctx, const NameAttrList& function,
    const XlaPlatformInfo& platform_info, absl::Span<const int> resources,
    absl::Span<const int> constants, bool lazy, xla::LocalClient** client,
    std::map<int, OptionalTensor>* variables,
    const XlaCompiler::CompilationResult** kernel,
    xla::LocalExecutable** executable) {
  // We store information about the JIT-compiled XLA computation
  // in the ResourceMgr.
  ResourceMgr* rm = ctx->resource_manager();
  if (!rm) {
    return errors::Internal("No resource manager.");
  }

  XlaCompilationCache* cache;
  TF_RETURN_IF_ERROR(rm->LookupOrCreate<XlaCompilationCache>(
      rm->default_container(), "xla_cache", &cache,
      [&](XlaCompilationCache** cache) {
        return BuildCompilationCache(ctx, platform_info, cache);
      }));
  // Hold the reference to the JIT during evaluation. (We could probably
  // free it sooner because the ResourceMgr will retain a reference, but
  // this is more obviously correct.)
  core::ScopedUnref cache_ref(cache);

  TF_RETURN_IF_ERROR(SnapshotResourceVariables(ctx, resources, variables));
  *client = static_cast<xla::LocalClient*>(cache->client());

  absl::optional<se::TfAllocatorAdapter> tf_allocator_adapter;
  XlaCompiler::Options options;
  options.client = *client;
  if (ctx->op_device_context() != nullptr) {
    options.device_ordinal =
        ctx->op_device_context()->stream()->parent()->device_ordinal();
  }
  options.device_type = cache->device_type();
  options.flib_def = ctx->function_library()->GetFunctionLibraryDefinition();
  options.graph_def_version = ctx->function_library()->graph_def_version();
  options.allow_cpu_custom_calls =
      (platform_info.platform_id() == se::host::kHostPlatformId);
  options.device_allocator =
      GetAllocator(&tf_allocator_adapter, ctx, platform_info);
  if (platform_info.xla_device_metadata()) {
    options.shape_representation_fn =
        platform_info.xla_device_metadata()->shape_representation_fn();
  }

  std::map<int, Tensor> constant_args;
  for (int i : constants) {
    constant_args.insert({i, ctx->input(i)});
  }
  XlaCompiler::CompileOptions compile_options;
  compile_options.is_entry_computation = true;
  // If we resolve constants we never emit them on the device, meaning that if
  // they are needed by a following computation the host has to transfer
  // them. Not resolving constants is expected to be faster than resolving
  // constants.
  compile_options.resolve_compile_time_constants = true;
  // Optimization: where possible, have the computation return a naked array
  // rather than a one-element tuple.
  compile_options.always_return_tuple = false;

  std::vector<XlaCompiler::Argument> args;
  TF_RETURN_IF_ERROR(XlaComputationLaunchContext::BuildXlaCompilerArguments(
      constant_args, *variables, ctx, &args));
  return cache->Compile(options, function, args, compile_options,
                        lazy ? XlaCompilationCache::CompileMode::kLazy
                             : XlaCompilationCache::CompileMode::kStrict,
                        kernel, executable);
}

void XlaLocalLaunchBase::Compute(OpKernelContext* ctx) {
  VLOG(1) << "XlaLocalLaunchOpBase::Compute "
          << Canonicalize(function_.name(), AttrSlice(&function_.attr()));

  xla::LocalClient* client;
  const XlaCompiler::CompilationResult* kernel;
  xla::LocalExecutable* executable;
  std::map<int, OptionalTensor> variables;

  {
    Status s = CompileToLocalExecutable(
        ctx, function_, platform_info_, resources_, constants_, /*lazy=*/false,
        &client, &variables, &kernel, &executable);
    if (!s.ok() && (platform_info_.device_type().type_string() == DEVICE_CPU ||
                    platform_info_.device_type().type_string() == DEVICE_GPU)) {
      // Suggest auto jit if the failure was with GPU or CPU.
      errors::AppendToMessage(&s,
                              xla::status_macros::kPossibleAutoJitAlternative);
    }

    OP_REQUIRES_OK(ctx, s);
  }

  se::Stream* stream =
      ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr;

  VLOG(1) << "Executing XLA Computation...";

  absl::optional<se::TfAllocatorAdapter> tf_allocator_adapter;
  se::DeviceMemoryAllocator* allocator =
      GetAllocator(&tf_allocator_adapter, ctx, platform_info_);
  XlaComputationLaunchContext launch_context(
      client, allocator,
      /*allocate_xla_tensors=*/platform_info_.is_on_xla_device(),
      platform_info_.UseMultipleStreams());
  launch_context.PopulateInputs(ctx, kernel, variables,
                                /*missing_ctx_input_prefix=*/0);

  // Execute the computation.
  VLOG(2) << "Executing computation.";
  xla::ExecutableRunOptions run_options;
  run_options.set_stream(stream);
  run_options.set_allocator(allocator);
  run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device());
  run_options.set_rng_seed(GetXLARandomSeed());
  Env* env = Env::Default();
  auto start_time = env->NowMicros();

  xla::StatusOr<xla::ScopedShapedBuffer> run_result;
  if (!stream || platform_info_.platform_id() == se::host::kHostPlatformId) {
    run_result = executable->Run(launch_context.arguments(), run_options);
  } else {
    run_result = executable->RunAsync(launch_context.arguments(), run_options);
  }
  OP_REQUIRES(ctx, run_result.ok(), run_result.status());

  auto elapsed = env->NowMicros() - start_time;
  VLOG(2) << "Elapsed time: " << elapsed << "us";

  OP_REQUIRES_OK(ctx, launch_context.PopulateOutputs(
                          ctx, kernel, run_result.ConsumeValueOrDie(),
                          /*missing_ctx_input_prefix=*/0));
  VLOG(1) << "Done";
}

namespace {
// Helper static functions to construct parameters for
// XlaLocalLaunchBase constructor from OpKernelConstruction.
std::vector<int> ConstantsVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));
  std::vector<int> constants(constant_types.size());
  std::iota(constants.begin(), constants.end(), 0);
  return constants;
}

std::vector<int> ResourcesVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));

  DataTypeVector arg_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Targs", &arg_types));

  int num_resources;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Nresources", &num_resources));

  std::vector<int> resources(num_resources);
  std::iota(resources.begin(), resources.end(),
            constant_types.size() + arg_types.size());
  return resources;
}

NameAttrList FunctionAttr(OpKernelConstruction* ctx) {
  const NameAttrList* func;
  OP_REQUIRES_OK_RETURN(ctx, NameAttrList(), ctx->GetAttr("function", &func));
  return *func;
}

bool MustCompileAttr(OpKernelConstruction* ctx) {
  bool must_compile;
  OP_REQUIRES_OK_RETURN(ctx, false,
                        ctx->GetAttr("must_compile", &must_compile));
  return must_compile;
}
}  // namespace

XlaLocalLaunchOp::XlaLocalLaunchOp(OpKernelConstruction* ctx)
    : XlaLocalLaunchBase(ctx, ConstantsVector(ctx), ResourcesVector(ctx),
                         FunctionAttr(ctx)) {}

XlaLocalLaunchOp::~XlaLocalLaunchOp() {
  VLOG(1) << "XlaLocalLaunchOp destroyed";
}

XlaCompileOp::XlaCompileOp(OpKernelConstruction* ctx)
    : OpKernel(ctx),
      constants_(ConstantsVector(ctx)),
      resources_(ResourcesVector(ctx)),
      function_(FunctionAttr(ctx)),
      platform_info_(PlatformInfoFromContext(ctx)),
      must_compile_(MustCompileAttr(ctx)) {}

void XlaCompileOp::Compute(OpKernelContext* ctx) {
  VLOG(3) << "XlaCompileOp " << def().name()
          << (must_compile_ ? "(must-compile)" : "");
  xla::LocalClient* client;
  const XlaCompiler::CompilationResult* kernel;
  xla::LocalExecutable* executable;
  std::map<int, OptionalTensor> variables;

  bool cannot_compile_cluster;
  {
    mutex_lock guard(cannot_compile_cluster_mu_);
    cannot_compile_cluster = cannot_compile_cluster_;
  }

  if (GetXlaOpsCommonFlags().tf_xla_always_defer_compilation ||
      cannot_compile_cluster) {
    executable = nullptr;
  } else {
    Status status = CompileToLocalExecutable(
        ctx, function_, platform_info_, resources_, constants_,
        /*lazy=*/!must_compile_, &client, &variables, &kernel, &executable);
    if (must_compile_ || status.code() != error::UNIMPLEMENTED) {
      OP_REQUIRES_OK(ctx, status);
    }

    if (status.code() == error::UNIMPLEMENTED) {
      LOG(WARNING) << "Compilation failed:" << status.ToString()
                   << ".  Falling back to TF function call.";

      BroadcastOptimizationRemark(
          XlaOptimizationRemark::UNIMPLEMENTED_OPERATION, status.ToString())
          .IgnoreError();
      executable = nullptr;
      mutex_lock guard(cannot_compile_cluster_mu_);
      cannot_compile_cluster_ = true;
    }
  }

  AllocatorAttributes host_alloc_attrs;
  host_alloc_attrs.set_gpu_compatible(true);
  host_alloc_attrs.set_on_host(true);
  Allocator* cpu_allocator = ctx->device()->GetAllocator(host_alloc_attrs);

  if (!executable) {
    DCHECK(!must_compile_);
    Tensor compilation_key(cpu_allocator, DT_STRING, TensorShape({}));

    Tensor compilation_successful(cpu_allocator, DT_BOOL, TensorShape({}));
    compilation_successful.scalar<bool>()() = false;
    ctx->set_output(0, Tensor(cpu_allocator, DT_STRING, TensorShape({})));
    ctx->set_output(1, compilation_successful);
    return;
  }

  // Each execution of an XlaCompile op creates a new XlaExecutableClosure, even
  // if it didn't have to compile the cluster because of a compilation-cache
  // hit.  This is because we at least need new snapshots of the resource
  // variables.
  XlaExecutableClosureStore::KeyT key =
      XlaExecutableClosureStore::Global()->Produce(XlaExecutableClosure(
          client, executable, kernel, std::move(variables), constants_.size()));

  Tensor compilation_key(cpu_allocator, DT_STRING, TensorShape({}));
  compilation_key.flat<tstring>()(0) = key;

  Tensor compilation_successful(cpu_allocator, DT_BOOL, TensorShape({}));
  compilation_successful.flat<bool>()(0) = true;

  ctx->set_output(0, compilation_key);
  ctx->set_output(1, compilation_successful);
}

XlaRunOp::XlaRunOp(OpKernelConstruction* ctx)
    : OpKernel(ctx), platform_info_(PlatformInfoFromContext(ctx)) {}

void XlaRunOp::Compute(OpKernelContext* ctx) {
  VLOG(3) << "XlaRunOp " << def().name();
  Tensor key_tensor = ctx->input(ctx->num_inputs() - 1);
  const XlaExecutableClosureStore::KeyT& key = key_tensor.flat<tstring>()(0);

  XlaExecutableClosure closure =
      XlaExecutableClosureStore::Global()->Consume(key);

  absl::optional<se::TfAllocatorAdapter> tf_allocator_adapter;
  se::DeviceMemoryAllocator* allocator =
      GetAllocator(&tf_allocator_adapter, ctx, platform_info_);
  XlaComputationLaunchContext launch_context(
      closure.client(), allocator,
      /*allocate_xla_tensors=*/platform_info_.is_on_xla_device(),
      /*use_multiple_streams=*/platform_info_.UseMultipleStreams());

  // We're missing the must-be-constant inputs, tell `PopulateInputs`
  // about this.  We don't actually need these inputs because they've
  // already been baked into the compiled kernel.
  {
    tensorflow::profiler::TraceMe hlo_module_activity(
        [&] {
          return absl::StrCat(
              "Populate Inputs (",
              closure.compilation_result()->xla_input_shapes.size(), ")");
        },
        tensorflow::profiler::TraceMeLevel::kInfo);

    launch_context.PopulateInputs(
        ctx, closure.compilation_result(), closure.resource_var_snapshots(),
        /*missing_ctx_input_prefix=*/closure.num_constant_args());
  }

  se::Stream* stream =
      ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr;
  xla::ExecutableRunOptions run_options;
  run_options.set_stream(stream);
  run_options.set_allocator(allocator);
  run_options.set_intra_op_thread_pool(&ctx->eigen_cpu_device());
  run_options.set_rng_seed(GetXLARandomSeed());
  Env* env = Env::Default();
  auto start_time = env->NowMicros();

  xla::StatusOr<xla::ScopedShapedBuffer> run_result;
  if (!stream || platform_info_.platform_id() == se::host::kHostPlatformId) {
    run_result =
        closure.executable()->Run(launch_context.arguments(), run_options);
  } else {
    run_result =
        closure.executable()->RunAsync(launch_context.arguments(), run_options);
  }
  OP_REQUIRES(ctx, run_result.ok(), run_result.status());

  auto elapsed = env->NowMicros() - start_time;
  VLOG(2) << "Elapsed time in computation: " << elapsed << "us";

  tensorflow::profiler::TraceMe hlo_module_activity(
      [&] {
        return absl::StrCat("Populate Outputs (", ctx->num_outputs(), ")");
      },
      tensorflow::profiler::TraceMeLevel::kInfo);

  OP_REQUIRES_OK(
      ctx,
      launch_context.PopulateOutputs(
          ctx, closure.compilation_result(), run_result.ConsumeValueOrDie(),
          /*missing_ctx_input_prefix=*/closure.num_constant_args()));
}

REGISTER_KERNEL_BUILDER(Name("XlaLaunch").Device(DEVICE_CPU), XlaLocalLaunchOp);

REGISTER_KERNEL_BUILDER(Name("XlaLaunch")
                            .Device(DEVICE_GPU)
                            .HostMemory("constants")
                            .HostMemory("resources"),
                        XlaLocalLaunchOp);

REGISTER_KERNEL_BUILDER(Name("_XlaCompile").Device(DEVICE_CPU), XlaCompileOp);
REGISTER_KERNEL_BUILDER(Name("_XlaCompile")
                            .Device(DEVICE_GPU)
                            .HostMemory("constants")
                            .HostMemory("key")
                            .HostMemory("compilation_successful")
                            .HostMemory("resources"),
                        XlaCompileOp);

REGISTER_KERNEL_BUILDER(Name("_XlaRun").Device(DEVICE_CPU), XlaRunOp);
REGISTER_KERNEL_BUILDER(Name("_XlaRun").Device(DEVICE_GPU), XlaRunOp);

}  // namespace tensorflow
