Fixed trainer total_flos relaoding in distributed mode (huggingface#1…

…1383)

* Fixed trainer total_flos relaoding in distributed mode

* logging flos at the end of training

src/transformers/trainer.py

@@ -426,9 +426,9 @@ def __init__(
 
         self.state = TrainerState()
         self.control = TrainerControl()
-        # Internal variable for total_flos used to count as tensors (for distributed + TPU), will be sent in the
-        # state at each call to self.log.
-        self._total_flos = None
+        # Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then
+        # returned to 0 every time flos need to be logged
+        self.current_flos = 0
         self.hp_search_backend = None
         self.use_tune_checkpoints = False
         default_label_names = (
@@ -1162,7 +1162,6 @@ def train(
         # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
         self._total_loss_scalar = 0.0
         self._globalstep_last_logged = self.state.global_step
-        self._total_flos = self.state.total_flos
         model.zero_grad()
 
         self.control = self.callback_handler.on_train_begin(args, self.state, self.control)
@@ -1220,7 +1219,7 @@ def train(
                         tr_loss += self.training_step(model, inputs)
                 else:
                     tr_loss += self.training_step(model, inputs)
-                self._total_flos += float(self.floating_point_ops(inputs))
+                self.current_flos += float(self.floating_point_ops(inputs))
 
                 # Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps
                 if self.deepspeed:
@@ -1321,9 +1320,8 @@ def train(
                 )
 
         metrics = speed_metrics("train", start_time, self.state.max_steps)
-        if self._total_flos is not None:
-            self.store_flos()
-            metrics["total_flos"] = self.state.total_flos
+        self.store_flos()
+        metrics["total_flos"] = self.state.total_flos
         self.log(metrics)
 
         self.control = self.callback_handler.on_train_end(args, self.state, self.control)
@@ -1788,11 +1786,12 @@ def _save(self, output_dir: Optional[str] = None, state_dict=None):
 
     def store_flos(self):
         # Storing the number of floating-point operations that went into the model
-        if self._total_flos is not None:
-            if self.args.local_rank != -1:
-                self.state.total_flos = distributed_broadcast_scalars([self._total_flos]).sum().item()
-            else:
-                self.state.total_flos = self._total_flos
+        if self.args.local_rank != -1:
+            self.state.total_flos += distributed_broadcast_scalars([self.current_flos]).sum().item()
+            self.current_flos = 0
+        else:
+            self.state.total_flos = self.current_flos
+            self.current_flos = 0
 
     def _sorted_checkpoints(
         self, output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False
@@ -1883,6 +1882,7 @@ def evaluate(
         )
 
         output.metrics.update(speed_metrics(metric_key_prefix, start_time, output.num_samples))
+
         self.log(output.metrics)
 
         if self.args.tpu_metrics_debug or self.args.debug: