Update WorkerCacheLogger::RecordRecvTensor

tensorflow/core/distributed_runtime/worker_cache_logger.cc

@@ -88,27 +88,39 @@ void WorkerCacheLogger::RecordRecvTensor(int64 step_id, int64 start_usecs,
                                          const string& src_device,
                                          const string& dst_device,
                                          int64 bytes) {
-  NodeExecStats* ns = new NodeExecStats;
-  ns->set_node_name("RecvTensor");
-  string byte_string = strings::StrCat("[", bytes, "B] ");
-  if (bytes >= 0.1 * 1048576.0) {
-    byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
-  }
-  ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
-                                         src_device, " to ", dst_device));
-  ns->set_all_start_micros(start_usecs);
-  ns->set_op_start_rel_micros(0);
-  int64 elapsed = end_usecs - start_usecs;
-  ns->set_op_end_rel_micros(elapsed);
-  ns->set_all_end_rel_micros(elapsed);
-  NodeOutput* no = ns->add_output();
-  no->set_slot(0);
-  // TODO(tucker): Maybe set the dimensions too, but then they'll
-  // need to be passed in.
-  no->mutable_tensor_description()
-      ->mutable_allocation_description()
-      ->set_requested_bytes(bytes);
-  Save(dst_device, step_id, ns);
+  RecordDataTransfer(step_id, start_usecs, end_usecs, tensor_name, src_device,
+    dst_device, bytes, "", "RecvTensor");
 }
 
+void WorkerCacheLogger::RecordDataTransfer(int64 step_id, int64 start_usecs,
+                                           int64 end_usecs,
+                                           const string& tensor_name,
+                                           const string& src_device,
+                                           const string& dst_device,
+                                           int64 bytes,
+                                           const string& details,
+                                           const string& transfer_method_name){
+  NodeExecStats* ns = new NodeExecStats;
+    ns->set_node_name(transfer_method_name);
+    string byte_string = strings::StrCat("[", bytes, "B] ");
+    if (bytes >= 0.1 * 1048576.0) {
+      byte_string = strings::Printf("[%.1fMB] ", bytes / 1048576.0);
+    }
+    ns->set_timeline_label(strings::StrCat(byte_string, tensor_name, " from ",
+                                           src_device, " to ", dst_device,
+                                           details));
+    ns->set_all_start_micros(start_usecs);
+    ns->set_op_start_rel_micros(0);
+    int64 elapsed = end_usecs - start_usecs;
+    ns->set_op_end_rel_micros(elapsed);
+    ns->set_all_end_rel_micros(elapsed);
+    NodeOutput* no = ns->add_output();
+    no->set_slot(0);
+    // TODO(tucker): Maybe set the dimensions too, but then they'll
+    // need to be passed in.
+    no->mutable_tensor_description()
+        ->mutable_allocation_description()
+        ->set_requested_bytes(bytes);
+    Save(dst_device, step_id, ns);
+  }
 }  // namespace tensorflow

tensorflow/core/distributed_runtime/worker_cache_logger.h

@@ -60,6 +60,14 @@ class WorkerCacheLogger {
                         const string& tensor_name, const string& src_device,
                         const string& dst_device, int64 bytes);
 
+  // Generates a NodeExecStats record with the given data, and saves for
+  // later retrieval by RetrieveLogs().
+  void RecordDataTransfer(int64 step_id, int64 start_usecs, int64 end_usecs,
+                          const string& tensor_name, const string& src_device,
+                          const string& dst_device, int64 bytes,
+                          const string& details,
+                          const string& transfer_method_name);
+
  private:
   mutex count_mu_;
   int32 want_logging_count_ GUARDED_BY(count_mu_) = 0;