Refactor XLA's CompileAheadOfTime out of LocalClient into a new CompileOnlyClient class, and likewise from LocalService into a new CompileOnlyService class.

This also renames AheadOfTimeComputationInstance to AotComputationInstance for consistency with AotCompilationResult and AotCompilationOptions in compiler/xla/service/compiler.h.
Change: 155252320

tensorflow/compiler/aot/BUILD

@@ -73,7 +73,7 @@ cc_library(
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla:xla_data_proto",
         "//tensorflow/compiler/xla/client:client_library",
-        "//tensorflow/compiler/xla/client:local_client",
+        "//tensorflow/compiler/xla/client:compile_only_client",
         "//tensorflow/compiler/xla/service:compiler",
         "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
         "//tensorflow/core:core_cpu",

tensorflow/compiler/aot/compile.cc

@@ -27,7 +27,7 @@ limitations under the License.
 #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/client/compile_only_client.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -274,7 +274,8 @@ Status CreateXlaArgs(const Graph& graph,
 
 // Converts the TensorFlow graph into an XLA computation, by executing the
 // graph symbolically, with each op building up the XLA HLO.
-Status ConvertGraphToXla(xla::LocalClient* client, std::unique_ptr<Graph> graph,
+Status ConvertGraphToXla(xla::CompileOnlyClient* client,
+                         std::unique_ptr<Graph> graph,
                          xla::Computation* computation, bool* has_context_arg) {
   // Create a device and context to convert the graph into an XLA computation.
   XlaOpRegistry::RegisterCompilationKernels();
@@ -333,7 +334,8 @@ Status ConvertGraphToXla(xla::LocalClient* client, std::unique_ptr<Graph> graph,
 }
 
 // Compiles the XLA computation into executable code.
-Status CompileXla(xla::LocalClient* client, const xla::Computation& computation,
+Status CompileXla(xla::CompileOnlyClient* client,
+                  const xla::Computation& computation,
                   const xla::cpu::CpuAotCompilationOptions& aot_opts,
                   CompileResult* compile_result) {
   // Retrieves arg and result layouts from the computation.
@@ -350,7 +352,7 @@ Status CompileXla(xla::LocalClient* client, const xla::Computation& computation,
   for (int i = 0; i < pshape->parameters_size(); ++i) {
     arg_layouts.push_back(pshape->mutable_parameters(i));
   }
-  xla::LocalClient::AheadOfTimeComputationInstance instance;
+  xla::CompileOnlyClient::AotComputationInstance instance;
   instance.computation = &computation;
   instance.argument_layouts = std::move(arg_layouts);
   instance.result_layout = &pshape->result();
@@ -365,7 +367,7 @@ Status CompileXla(xla::LocalClient* client, const xla::Computation& computation,
           std::move(aot_or.ValueOrDie().back()));
   compile_result->entry_point = aot_opts.entry_point_name();
   compile_result->pointer_size =
-      xla::LocalClient::PointerSizeForTriple(aot_opts.triple());
+      xla::CompileOnlyClient::PointerSizeForTriple(aot_opts.triple());
   return Status::OK();
 }
 
@@ -394,8 +396,9 @@ Status CompileGraph(std::unique_ptr<Graph> graph, const MainFlags& flags,
   namespace gpu = perftools::gputools;
   gpu::Platform* cpu_platform =
       gpu::MultiPlatformManager::PlatformWithName("Host").ValueOrDie();
-  xla::LocalClient* client =
+  xla::CompileOnlyClient* client =
-      xla::ClientLibrary::GetOrCreateLocalClient(cpu_platform).ValueOrDie();
+      xla::ClientLibrary::GetOrCreateCompileOnlyClient(cpu_platform)
+          .ValueOrDie();
   xla::Computation computation;
   TF_RETURN_IF_ERROR(ConvertGraphToXla(client, std::move(graph), &computation,
                                        &compile_result->has_context_arg));

tensorflow/compiler/xla/client/BUILD

@@ -99,6 +99,26 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "compile_only_client",
+    srcs = ["compile_only_client.cc"],
+    hdrs = ["compile_only_client.h"],
+    deps = [
+        ":client",
+        ":computation",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/compiler/xla/service:compile_only_service",
+        "//tensorflow/compiler/xla/service:compiler",
+        "//tensorflow/compiler/xla/service/llvm_ir:llvm_util",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:stream_executor_no_cuda",
+        "@llvm//:support",
+    ],
+)
+
 # This target is used to instantiate the XLA service in-process and create
 # a client for it.
 cc_library(
@@ -106,12 +126,14 @@ cc_library(
     srcs = ["client_library.cc"],
     hdrs = ["client_library.h"],
     deps = [
+        ":compile_only_client",
         ":local_client",
         "//tensorflow/compiler/xla:status_macros",
         "//tensorflow/compiler/xla:statusor",
         "//tensorflow/compiler/xla:types",
         "//tensorflow/compiler/xla:util",
         "//tensorflow/compiler/xla/service:backend",
+        "//tensorflow/compiler/xla/service:compile_only_service",
         "//tensorflow/compiler/xla/service:device_memory_allocator",
         "//tensorflow/compiler/xla/service:local_service",
         "//tensorflow/compiler/xla/service:platform_util",

tensorflow/compiler/xla/client/client_library.cc

@@ -69,8 +69,8 @@ ClientLibrary::~ClientLibrary() = default;
     TF_ASSIGN_OR_RETURN(platform, PlatformUtil::GetDefaultPlatform());
   }
 
-  auto it = client_library.instances_.find(platform->id());
+  auto it = client_library.local_instances_.find(platform->id());
-  if (it != client_library.instances_.end()) {
+  if (it != client_library.local_instances_.end()) {
     return it->second->client.get();
   }
 
@@ -78,13 +78,13 @@ ClientLibrary::~ClientLibrary() = default;
   service_options.set_platform(platform);
   service_options.set_number_of_replicas(replica_count);
 
-  std::unique_ptr<LocalInstance> instance = MakeUnique<LocalInstance>();
+  auto instance = MakeUnique<LocalInstance>();
   TF_ASSIGN_OR_RETURN(instance->service,
                       LocalService::NewService(service_options));
   instance->client = MakeUnique<LocalClient>(instance->service.get());
   LocalClient* cl = instance->client.get();
 
-  client_library.instances_.insert(
+  client_library.local_instances_.insert(
       std::make_pair(platform->id(), std::move(instance)));
   return cl;
 }
@@ -99,9 +99,35 @@ ClientLibrary::~ClientLibrary() = default;
     perftools::gputools::Platform* platform) {
   ClientLibrary& client_library = Singleton();
   tensorflow::mutex_lock lock(client_library.service_mutex_);
-  auto it = client_library.instances_.find(platform->id());
+  auto it = client_library.local_instances_.find(platform->id());
-  CHECK(it != client_library.instances_.end());
+  CHECK(it != client_library.local_instances_.end());
   return it->second->service.get();
 }
 
+/* static */ StatusOr<CompileOnlyClient*>
+ClientLibrary::GetOrCreateCompileOnlyClient(
+    perftools::gputools::Platform* platform) {
+  ClientLibrary& client_library = Singleton();
+  tensorflow::mutex_lock lock(client_library.service_mutex_);
+
+  if (platform == nullptr) {
+    TF_ASSIGN_OR_RETURN(platform, PlatformUtil::GetDefaultPlatform());
+  }
+
+  auto it = client_library.compile_only_instances_.find(platform->id());
+  if (it != client_library.compile_only_instances_.end()) {
+    return it->second->client.get();
+  }
+
+  auto instance = MakeUnique<CompileOnlyInstance>();
+  TF_ASSIGN_OR_RETURN(instance->service,
+                      CompileOnlyService::NewService(platform));
+  instance->client = MakeUnique<CompileOnlyClient>(instance->service.get());
+  CompileOnlyClient* cl = instance->client.get();
+
+  client_library.compile_only_instances_.insert(
+      std::make_pair(platform->id(), std::move(instance)));
+  return cl;
+}
+
 }  // namespace xla

tensorflow/compiler/xla/client/client_library.h

@@ -26,7 +26,9 @@ limitations under the License.
 #include <string>
 #include <vector>
 
+#include "tensorflow/compiler/xla/client/compile_only_client.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
+#include "tensorflow/compiler/xla/service/compile_only_service.h"
 #include "tensorflow/compiler/xla/service/device_memory_allocator.h"
 #include "tensorflow/compiler/xla/service/local_service.h"
 #include "tensorflow/compiler/xla/statusor.h"
@@ -76,6 +78,13 @@ class ClientLibrary {
   // access user computations from client.
   static LocalService* GetXlaService(perftools::gputools::Platform* platform);
 
+  // Singleton constructor-or-accessor for compile-only clients. Arguments:
+  //
+  //   platform : The platform the underlying XLA service should target. If
+  //     null then default platform is used.
+  static StatusOr<CompileOnlyClient*> GetOrCreateCompileOnlyClient(
+      perftools::gputools::Platform* platform = nullptr);
+
  private:
   // Returns the singleton instance of ClientLibrary.
   static ClientLibrary& Singleton();
@@ -90,10 +99,21 @@ class ClientLibrary {
     std::unique_ptr<LocalClient> client;
   };
 
+  struct CompileOnlyInstance {
+    // Service that is wrapped by the singleton client object.
+    std::unique_ptr<CompileOnlyService> service;
+    // Singleton client object.
+    std::unique_ptr<CompileOnlyClient> client;
+  };
+
   tensorflow::mutex service_mutex_;  // Guards the singleton creation state.
   std::unordered_map<perftools::gputools::Platform::Id,
                      std::unique_ptr<LocalInstance>>
-      instances_ GUARDED_BY(service_mutex_);
+      local_instances_ GUARDED_BY(service_mutex_);
+
+  std::unordered_map<perftools::gputools::Platform::Id,
+                     std::unique_ptr<CompileOnlyInstance>>
+      compile_only_instances_ GUARDED_BY(service_mutex_);
 
   TF_DISALLOW_COPY_AND_ASSIGN(ClientLibrary);
 };

tensorflow/compiler/xla/client/compile_only_client.cc

@@ -0,0 +1,59 @@
+/* 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/xla/client/compile_only_client.h"
+
+#include "external/llvm/include/llvm/ADT/Triple.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+
+StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+CompileOnlyClient::CompileAheadOfTime(
+    const tensorflow::gtl::ArraySlice<AotComputationInstance> computations,
+    const AotCompilationOptions& options) {
+  std::vector<CompileOnlyService::AotComputationInstance> service_instances;
+  service_instances.reserve(computations.size());
+  for (const AotComputationInstance& instance : computations) {
+    service_instances.push_back({});
+    CompileOnlyService::AotComputationInstance& service_instance =
+        service_instances.back();
+    TF_RET_CHECK(instance.computation != nullptr);
+    service_instance.computation = instance.computation->handle();
+    service_instance.argument_layouts = instance.argument_layouts;
+    service_instance.result_layout = instance.result_layout;
+  }
+  return compiler_service_->CompileAheadOfTime(service_instances, options);
+}
+
+int64 CompileOnlyClient::PointerSizeForTriple(
+    tensorflow::StringPiece target_triple) {
+  llvm::Triple triple(
+      llvm::Triple::normalize(llvm_ir::AsStringRef(target_triple)));
+  if (triple.isArch64Bit()) {
+    return 8;
+  } else if (triple.isArch32Bit()) {
+    return 4;
+  } else {
+    CHECK(triple.isArch16Bit());
+    return 2;
+  }
+}
+
+}  // namespace xla

tensorflow/compiler/xla/client/compile_only_client.h

@@ -0,0 +1,66 @@
+/* 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.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_COMPILE_ONLY_CLIENT_H_
+#define TENSORFLOW_COMPILER_XLA_CLIENT_COMPILE_ONLY_CLIENT_H_
+
+#include "tensorflow/compiler/xla/client/client.h"
+#include "tensorflow/compiler/xla/client/computation.h"
+#include "tensorflow/compiler/xla/service/compile_only_service.h"
+#include "tensorflow/compiler/xla/service/compiler.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+namespace xla {
+
+// An XLA Client specialization for doing ahead-of-time compilation.  This does
+// not require (or attempt to instantiate) an execution-capable backend for the
+// relevant platform.
+class CompileOnlyClient : public Client {
+ public:
+  explicit CompileOnlyClient(CompileOnlyService* service)
+      : Client(service), compiler_service_(service) {}
+
+  CompileOnlyClient(const CompileOnlyClient&) = delete;
+  void operator=(const CompileOnlyClient&) = delete;
+
+  // A description of a computation to compile using CompileAheadOfTime.
+  struct AotComputationInstance {
+    const Computation* computation;
+    // Inform the compiler of the expected layout for arguments.
+    std::vector<const Shape*> argument_layouts;
+    // Specifies the expected result layout.
+    const Shape* result_layout;
+  };
+
+  // Compiles a list of computations for ahead-of-time execution.  This is
+  // intended for use in static compilation. The |options| parameter describes
+  // the target for which the compiler should emit code.
+  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+  CompileAheadOfTime(
+      const tensorflow::gtl::ArraySlice<AotComputationInstance> computations,
+      const AotCompilationOptions& options);
+
+  // Returns the size of a pointer in bytes for a given triple.
+  static int64 PointerSizeForTriple(tensorflow::StringPiece triple);
+
+ private:
+  CompileOnlyService* compiler_service_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_CLIENT_COMPILE_ONLY_CLIENT_H_

tensorflow/compiler/xla/client/local_client.cc

@@ -261,38 +261,6 @@ tensorflow::Status LocalClient::ResolveArguments(
                                           argument_ptrs);
 }
 
-StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-LocalClient::CompileAheadOfTime(
-    const tensorflow::gtl::ArraySlice<AheadOfTimeComputationInstance>
-        computations,
-    const AotCompilationOptions& options) {
-  std::vector<LocalService::AheadOfTimeComputationInstance> service_instances;
-  service_instances.reserve(computations.size());
-  for (const AheadOfTimeComputationInstance& instance : computations) {
-    service_instances.push_back({});
-    LocalService::AheadOfTimeComputationInstance& service_instance =
-        service_instances.back();
-    TF_RET_CHECK(instance.computation != nullptr);
-    service_instance.computation = instance.computation->handle();
-    service_instance.argument_layouts = instance.argument_layouts;
-    service_instance.result_layout = instance.result_layout;
-  }
-  return local_service_->CompileAheadOfTime(service_instances, options);
-}
-
-int64 LocalClient::PointerSizeForTriple(tensorflow::StringPiece target_triple) {
-  llvm::Triple triple(
-      llvm::Triple::normalize(llvm_ir::AsStringRef(target_triple)));
-  if (triple.isArch64Bit()) {
-    return 8;
-  } else if (triple.isArch32Bit()) {
-    return 4;
-  } else {
-    CHECK(triple.isArch16Bit());
-    return 2;
-  }
-}
-
 se::Platform* LocalClient::platform() const {
   return local_service_->backend().platform();
 }

tensorflow/compiler/xla/client/local_client.h

@@ -148,7 +148,7 @@ class LocalExecutable {
   const ExecutableBuildOptions& build_options_;
 };
 
-// An XLA service client object for use when the client and service run in
+// An XLA Client specialization for use when the client and service run in
 // the same process.
 class LocalClient : public Client {
  public:
@@ -182,30 +182,6 @@ class LocalClient : public Client {
       const tensorflow::gtl::ArraySlice<const Shape*> argument_layouts,
       const ExecutableBuildOptions& options);
 
-  // A description of a computation to compile using CompileAheadOfTime.
-  struct AheadOfTimeComputationInstance {
-    const Computation* computation;
-    // Inform the compiler of the expected layout for arguments.
-    std::vector<const Shape*> argument_layouts;
-    // Specifies the expected result layout.
-    const Shape* result_layout;
-  };
-
-  // Compiles a list of computations for ahead-of-time execution.  This is
-  // intended for use in static compilation. The |options| parameter describes
-  // the target for which the compiler should emit code.
-  //
-  // TODO(b/31222190): This doesn't really belong in LocalClient. Move it to its
-  // own library.
-  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-  CompileAheadOfTime(
-      const tensorflow::gtl::ArraySlice<AheadOfTimeComputationInstance>
-          computations,
-      const AotCompilationOptions& options);
-
-  // Returns the size of a pointer in bytes for a given triple.
-  static int64 PointerSizeForTriple(tensorflow::StringPiece triple);
-
   // Returns the platform that the underlying service targets.
   perftools::gputools::Platform* platform() const;
 

tensorflow/compiler/xla/service/BUILD

@@ -408,6 +408,27 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "compile_only_service",
+    srcs = ["compile_only_service.cc"],
+    hdrs = ["compile_only_service.h"],
+    deps = [
+        ":backend",
+        ":compiler",
+        ":computation_layout",
+        ":computation_tracker",
+        ":platform_util",
+        ":service",
+        "//tensorflow/compiler/xla:status_macros",
+        "//tensorflow/compiler/xla:statusor",
+        "//tensorflow/compiler/xla:types",
+        "//tensorflow/compiler/xla:util",
+        "//tensorflow/compiler/xla:xla_data_proto",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:stream_executor_no_cuda",
+    ],
+)
+
 cc_library(
     name = "cpu_plugin",
     deps = [

tensorflow/compiler/xla/service/compile_only_service.cc

@@ -0,0 +1,131 @@
+/* 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/xla/service/compile_only_service.h"
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "tensorflow/compiler/xla/service/backend.h"
+#include "tensorflow/compiler/xla/service/computation_layout.h"
+#include "tensorflow/compiler/xla/service/computation_tracker.h"
+#include "tensorflow/compiler/xla/service/platform_util.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/gtl/cleanup.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+
+/* static */ StatusOr<std::unique_ptr<CompileOnlyService>>
+CompileOnlyService::NewService(perftools::gputools::Platform* platform) {
+  ServiceOptions default_options;
+  default_options.set_platform(platform);
+  return NewService(default_options);
+}
+
+/* static */ StatusOr<std::unique_ptr<CompileOnlyService>>
+CompileOnlyService::NewService(const ServiceOptions& options) {
+  perftools::gputools::Platform* platform = options.platform();
+  if (platform == nullptr) {
+    TF_ASSIGN_OR_RETURN(platform, PlatformUtil::GetDefaultPlatform());
+  }
+
+  TF_ASSIGN_OR_RETURN(auto compiler, Compiler::GetForPlatform(platform));
+
+  TF_ASSIGN_OR_RETURN(std::unique_ptr<Backend> compute_constant_backend,
+                      CreateComputeConstantBackend());
+  std::unique_ptr<CompileOnlyService> service(
+      new CompileOnlyService(compiler, std::move(compute_constant_backend)));
+  return std::move(service);
+}
+
+CompileOnlyService::CompileOnlyService(
+    Compiler* compiler, std::unique_ptr<Backend> compute_constant_backend)
+    : Service(/*backend=*/nullptr, std::move(compute_constant_backend)),
+      compiler_(compiler) {
+  runs_in_client_process_ = true;
+}
+
+StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+CompileOnlyService::CompileAheadOfTime(
+    const tensorflow::gtl::ArraySlice<AotComputationInstance> computations,
+    const AotCompilationOptions& options) {
+  std::vector<std::unique_ptr<HloModule>> hlo_modules;
+  std::vector<std::unique_ptr<HloModuleConfig>> module_configs;
+  for (const AotComputationInstance& instance : computations) {
+    TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
+                        computation_tracker_.Resolve(instance.computation));
+    VersionedComputationHandle versioned_handle =
+        user_computation->GetVersionedHandle();
+
+    // Dump computation proto state if flag is set.
+    legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
+    const string& directory_path = flags->xla_dump_computations_to;
+    if (!directory_path.empty()) {
+      TF_ASSIGN_OR_RETURN(
+          std::unique_ptr<SessionModule> session_module,
+          computation_tracker_.SnapshotComputation(versioned_handle.handle));
+      string filename = tensorflow::strings::StrCat(
+          "computation_", versioned_handle.handle.handle(), "__",
+          session_module->entry().name(), "__version_",
+          versioned_handle.version);
+      TF_RETURN_IF_ERROR(Executable::DumpToDirectory(directory_path, filename,
+                                                     *session_module));
+    }
+
+    TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModule> hlo_module,
+                        computation_tracker_.BuildHloModule(
+                            versioned_handle,
+                            /*include_unreachable_instructions=*/true));
+    hlo_modules.push_back(std::move(hlo_module));
+
+    TF_ASSIGN_OR_RETURN(
+        std::shared_ptr<const ProgramShape> program_shape,
+        user_computation->ComputeProgramShape(versioned_handle.version));
+
+    module_configs.push_back(MakeUnique<HloModuleConfig>(*program_shape));
+    HloModuleConfig* module_config = module_configs.back().get();
+    auto* computation_layout =
+        module_config->mutable_entry_computation_layout();
+    if (flags->xla_hlo_profile) {
+      module_config->enable_hlo_profiling(true);
+    }
+    for (int i = 0; i < instance.argument_layouts.size(); ++i) {
+      const Shape& argument_layout = *instance.argument_layouts[i];
+      if (ShapeUtil::IsTuple(argument_layout)) {
+        return Unimplemented("tuple arguments not supported yet");
+      }
+      TF_RETURN_IF_ERROR(
+          computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape(
+              argument_layout));
+    }
+    TF_RETURN_IF_ERROR(
+        computation_layout->mutable_result_layout()->CopyLayoutFromShape(
+            *instance.result_layout));
+  }
+
+  return compiler_->CompileAheadOfTime(std::move(hlo_modules),
+                                       std::move(module_configs),
+                                       MakeHloDumper(), options);
+}
+
+}  // namespace xla

tensorflow/compiler/xla/service/compile_only_service.h

@@ -0,0 +1,128 @@
+/* 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.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_COMPILE_ONLY_SERVICE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_COMPILE_ONLY_SERVICE_H_
+
+#include "tensorflow/compiler/xla/service/backend.h"
+#include "tensorflow/compiler/xla/service/compiler.h"
+#include "tensorflow/compiler/xla/service/service.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/xla_data.pb.h"
+#include "tensorflow/core/platform/stream_executor_no_cuda.h"
+
+namespace xla {
+
+// An XLA Service specialization for ahead-of-time compilation.  This only
+// instantiates a Compiler object for the relevant platform; it does not
+// instantiate or require an execution backend.
+class CompileOnlyService : public Service {
+ public:
+  // Factory for creating a CompileOnlyService. The parameter platform is the
+  // platform that the service should target. If platform is null then the
+  // default platform is used.
+  static StatusOr<std::unique_ptr<CompileOnlyService>> NewService(
+      perftools::gputools::Platform* platform);
+  static StatusOr<std::unique_ptr<CompileOnlyService>> NewService(
+      const ServiceOptions& options);
+
+  // A description of a computation to compile using CompileAheadOfTime.
+  struct AotComputationInstance {
+    ComputationHandle computation;
+    std::vector<const Shape*> argument_layouts;
+    const Shape* result_layout = nullptr;
+  };
+
+  // Compiles a list of computations for ahead-of-time execution.  This is
+  // intended for use in static compilation.  See
+  // |CompileOnlyClient::CompileAheadOfTime| for additional details.
+  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
+  CompileAheadOfTime(
+      const tensorflow::gtl::ArraySlice<AotComputationInstance> computations,
+      const AotCompilationOptions& Options);
+
+  // Override Service methods that require an execute backend.
+  tensorflow::Status Execute(const ExecuteRequest* arg,
+                             ExecuteResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support execution.");
+  }
+  tensorflow::Status ExecuteParallel(const ExecuteParallelRequest* arg,
+                                     ExecuteParallelResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support execution.");
+  }
+  tensorflow::Status GetDeviceHandles(
+      const GetDeviceHandlesRequest* arg,
+      GetDeviceHandlesResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support devices.");
+  }
+  tensorflow::Status ExecuteAsync(const ExecuteAsyncRequest* arg,
+                                  ExecuteAsyncResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support execution.");
+  }
+  tensorflow::Status WaitForExecution(
+      const WaitForExecutionRequest* arg,
+      WaitForExecutionResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support execution.");
+  }
+  tensorflow::Status TransferToClient(
+      const TransferToClientRequest* arg,
+      TransferToClientResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status TransferToClientInProcess(
+      const TransferToClientInProcessRequest* arg,
+      TransferToClientInProcessResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status TransferToServer(
+      const TransferToServerRequest* arg,
+      TransferToServerResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status TransferToInfeed(
+      const TransferToInfeedRequest* arg,
+      TransferToInfeedResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status TransferFromOutfeed(
+      const TransferFromOutfeedRequest* arg,
+      TransferFromOutfeedResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status TransferToServerInProcess(
+      const TransferToServerInProcessRequest* arg,
+      TransferToServerInProcessResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support data transfers.");
+  }
+  tensorflow::Status ResetDevice(const ResetDeviceRequest* arg,
+                                 ResetDeviceResponse* result) override {
+    return Unimplemented("CompileOnlyService does not support devices.");
+  }
+
+ private:
+  explicit CompileOnlyService(
+      Compiler* compiler, std::unique_ptr<Backend> compute_constant_backend);
+  CompileOnlyService(const CompileOnlyService&) = delete;
+  void operator=(const CompileOnlyService&) = delete;
+
+  // The compiler for the target platform.  This is included in place of
+  // the Service::execute_backend_'s compiler, since execute_backend_ is a
+  // nullptr in CompileOnlyService.
+  Compiler* compiler_;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_COMPILE_ONLY_SERVICE_H_

tensorflow/compiler/xla/service/local_service.cc

@@ -128,70 +128,6 @@ StatusOr<GlobalDataHandle> LocalService::AllocateBufferOnDevice(
                                   allocation_size));
 }
 
-StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-LocalService::CompileAheadOfTime(
-    const tensorflow::gtl::ArraySlice<AheadOfTimeComputationInstance>
-        computations,
-    const AotCompilationOptions& options) {
-  std::vector<std::unique_ptr<HloModule>> hlo_modules;
-  std::vector<std::unique_ptr<HloModuleConfig>> module_configs;
-  for (const AheadOfTimeComputationInstance& instance : computations) {
-    TF_ASSIGN_OR_RETURN(UserComputation * user_computation,
-                        computation_tracker_.Resolve(instance.computation));
-    VersionedComputationHandle versioned_handle =
-        user_computation->GetVersionedHandle();
-
-    // Dump computation proto state if flag is set.
-    legacy_flags::ServiceFlags* flags = legacy_flags::GetServiceFlags();
-    const string& directory_path = flags->xla_dump_computations_to;
-    if (!directory_path.empty()) {
-      TF_ASSIGN_OR_RETURN(
-          std::unique_ptr<SessionModule> session_module,
-          computation_tracker_.SnapshotComputation(versioned_handle.handle));
-      string filename = tensorflow::strings::StrCat(
-          "computation_", versioned_handle.handle.handle(), "__",
-          session_module->entry().name(), "__version_",
-          versioned_handle.version);
-      TF_RETURN_IF_ERROR(Executable::DumpToDirectory(directory_path, filename,
-                                                     *session_module));
-    }
-
-    TF_ASSIGN_OR_RETURN(std::unique_ptr<HloModule> hlo_module,
-                        computation_tracker_.BuildHloModule(
-                            versioned_handle,
-                            /*include_unreachable_instructions=*/true));
-    hlo_modules.push_back(std::move(hlo_module));
-
-    TF_ASSIGN_OR_RETURN(
-        std::shared_ptr<const ProgramShape> program_shape,
-        user_computation->ComputeProgramShape(versioned_handle.version));
-
-    module_configs.push_back(MakeUnique<HloModuleConfig>(*program_shape));
-    HloModuleConfig* module_config = module_configs.back().get();
-    auto* computation_layout =
-        module_config->mutable_entry_computation_layout();
-    if (flags->xla_hlo_profile) {
-      module_config->enable_hlo_profiling(true);
-    }
-    for (int i = 0; i < instance.argument_layouts.size(); ++i) {
-      const Shape& argument_layout = *instance.argument_layouts[i];
-      if (ShapeUtil::IsTuple(argument_layout)) {
-        return Unimplemented("tuple arguments not supported yet");
-      }
-      TF_RETURN_IF_ERROR(
-          computation_layout->mutable_parameter_layout(i)->CopyLayoutFromShape(
-              argument_layout));
-    }
-    TF_RETURN_IF_ERROR(
-        computation_layout->mutable_result_layout()->CopyLayoutFromShape(
-            *instance.result_layout));
-  }
-
-  return execute_backend_->compiler()->CompileAheadOfTime(
-      std::move(hlo_modules), std::move(module_configs), MakeHloDumper(),
-      options);
-}
-
 StatusOr<std::unique_ptr<Executable>> LocalService::CompileExecutable(
     const ComputationHandle& computation,
     const tensorflow::gtl::ArraySlice<const Shape*> argument_layouts,

tensorflow/compiler/xla/service/local_service.h

@@ -59,22 +59,6 @@ class LocalService : public Service {
       const Shape& shape, int device_ordinal,
       bool allocate_space_for_deep_copy);
 
-  // A description of a computation to compile using CompileAheadOfTime.
-  struct AheadOfTimeComputationInstance {
-    ComputationHandle computation;
-    std::vector<const Shape*> argument_layouts;
-    const Shape* result_layout = nullptr;
-  };
-
-  // Compiles a list of computations for ahead-of-time execution.  This is
-  // intended for use in static compilation.  See
-  // |LocalClient::CompileAheadOfTime| for additional details.
-  StatusOr<std::vector<std::unique_ptr<AotCompilationResult>>>
-  CompileAheadOfTime(
-      const tensorflow::gtl::ArraySlice<AheadOfTimeComputationInstance>
-          computations,
-      const AotCompilationOptions& Options);
-
   // Builds an Executable with the given argument layouts and options. If
   // result_layout is non-null, then the executable is compiled to produce a
   // result of the given layout.

tensorflow/compiler/xla/service/service.cc

@@ -180,20 +180,24 @@ Service::Service(std::unique_ptr<Backend> execute_backend,
                  std::unique_ptr<Backend> compute_constant_backend)
     : execute_backend_(std::move(execute_backend)),
       compute_constant_backend_(std::move(compute_constant_backend)) {
-  LOG(INFO) << Printf(
+  if (execute_backend_) {
-      "XLA service %p executing computations on platform %s. Devices:", this,
+    LOG(INFO) << Printf(
-      execute_backend_->platform()->Name().c_str());
+        "XLA service %p executing computations on platform %s. Devices:", this,
-  for (int i = 0; i < execute_backend_->device_count(); ++i) {
+        execute_backend_->platform()->Name().c_str());
-    if (execute_backend_->device_ordinal_supported(i)) {
+    for (int i = 0; i < execute_backend_->device_count(); ++i) {
-      se::StreamExecutor* executor =
+      if (execute_backend_->device_ordinal_supported(i)) {
-          execute_backend_->stream_executor(i).ValueOrDie();
+        se::StreamExecutor* executor =
-      const auto& description = executor->GetDeviceDescription();
+            execute_backend_->stream_executor(i).ValueOrDie();
-      LOG(INFO) << Printf("  StreamExecutor device (%d): %s, %s", i,
+        const auto& description = executor->GetDeviceDescription();
-                          description.name().c_str(),
+        LOG(INFO) << Printf("  StreamExecutor device (%d): %s, %s", i,
-                          description.platform_version().c_str());
+                            description.name().c_str(),
-    } else {
+                            description.platform_version().c_str());
-      LOG(INFO) << Printf("  StreamExecutor device (%d) not supported", i);
+      } else {
+        LOG(INFO) << Printf("  StreamExecutor device (%d) not supported", i);
+      }
     }
+  } else {
+    VLOG(1) << "XLA compile-only service constructed";
   }
 }
 

tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc

@@ -42,7 +42,7 @@ xla::Computation Doubler(xla::Client* client) {
 int main(int argc, char** argv) {
   tensorflow::port::InitMain(argv[0], &argc, &argv);
 
-  auto client = xla::ClientLibrary::LocalClientOrDie();
+  auto client = xla::ClientLibrary::GetOrCreateCompileOnlyClient().ValueOrDie();
 
   xla::ComputationBuilder builder(client, "aot_test_helper");
   auto opaque_shape = xla::ShapeUtil::MakeOpaqueShape();
@@ -74,7 +74,7 @@ int main(int argc, char** argv) {
   llvm::Triple triple(xla::llvm_ir::AsStringRef(triple_string));
 
   xla::Computation computation = builder.Build().ConsumeValueOrDie();
-  xla::LocalClient::AheadOfTimeComputationInstance instance{
+  xla::CompileOnlyClient::AotComputationInstance instance{
       &computation, /*argument_layouts=*/{&opaque_shape}, &r0f32};
 
   xla::cpu::CpuAotCompilationOptions options(

tensorflow/opensource_only/eigen.threadpool

@@ -0,0 +1 @@
+#include "unsupported/Eigen/CXX11/ThreadPool"