Automated g4 rollback of changelist 195379693

PiperOrigin-RevId: 198654780

diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
index 7d706b5..f6fa45a 100644 (file)
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.cc
@@ -247,6 +247,13 @@ tensorflow::gtl::optional<int64> HloModuleGroupMetadata::GetInstructionDevice(
   return device;
 }
 
+int64 HloModuleGroupMetadata::GetDeviceModulesCount() const {
+  return std::count_if(modules_.begin(), modules_.end(),
+                       [](const HloModule* module) {
+                         return !module->config().is_host_module();
+                       });
+}
+
 Status HloModuleGroupMetadata::RecordInstructions() {
   const auto visitor = [this](HloInstruction* hlo) -> Status {
     if (hlo->opcode() == HloOpcode::kWhile) {

diff --git a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
index 5f5bf27..f68d402 100644 (file)
--- a/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
+++ b/tensorflow/compiler/xla/service/hlo_module_group_metadata.h
@@ -155,6 +155,9 @@ class HloModuleGroupMetadata {
   tensorflow::gtl::optional<int64> GetInstructionDevice(
       const HloInstruction& instruction) const;
 
+  // Returns the number of modules for devices (excluding the host module).
+  int64 GetDeviceModulesCount() const;
+
   // Returns the companion instructions for the given instruction.
   //
   // Precondition: IsCompanionWhile(instruction) is true.

diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc
index cb0f76e..5a813dc 100644 (file)
--- a/tensorflow/compiler/xla/service/service.cc
+++ b/tensorflow/compiler/xla/service/service.cc
@@ -624,9 +624,16 @@ Service::ExecuteParallelAndRegisterResult(
   // profiled.
   std::map<int64, se::Stream*> index_to_profiled_streams;
 
-  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
-                      backend->computation_placer()->AssignDevices(
-                          options_.number_of_replicas(), executables.size()));
+  // Build DeviceAssignment for all cores based on the provided device handles.
+  DeviceAssignment device_assignment(options_.number_of_replicas(),
+                                     executables.size());
+  for (int64 i = 0; i < executables.size(); i++) {
+    TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, device_handles[i]));
+    CHECK_EQ(replicas.size(), arguments[i].size());
+    for (int64 replica = 0; replica < replicas.size(); ++replica) {
+      device_assignment(replica, i) = replicas[replica]->device_ordinal();
+    }
+  }
 
   for (int64 i = 0; i < executables.size(); i++) {
     // Stream executors for the replicas of the current computation.