Reload checkpoint (#7984)

* Fix checkpoint loading in Trainer

* Fix typo

src/transformers/trainer.py

@@ -628,18 +628,7 @@ class Trainer:
         self.state.is_hyper_param_search = trial is not None
 
         # Check if saved optimizer or scheduler states exist
-        if (
-            model_path is not None
-            and os.path.isfile(os.path.join(model_path, "optimizer.pt"))
-            and os.path.isfile(os.path.join(model_path, "scheduler.pt"))
-        ):
-            # Load in optimizer and scheduler states
-            self.optimizer.load_state_dict(
-                torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
-            )
-            with warnings.catch_warnings(record=True) as caught_warnings:
-                self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
-            reissue_pt_warnings(caught_warnings)
+        self._load_optimizer_and_scheduler(model_path)
 
         # Mixed precision training with apex (torch < 1.6)
         model = self.model
@@ -919,6 +908,34 @@ class Trainer:
         if self.is_world_process_zero():
             self._rotate_checkpoints(use_mtime=True)
 
+    def _load_optimizer_and_scheduler(self, model_path):
+        """If optimizer and scheduler states exist, load them."""
+        if (
+            model_path is not None
+            and os.path.isfile(os.path.join(model_path, "optimizer.pt"))
+            and os.path.isfile(os.path.join(model_path, "scheduler.pt"))
+        ):
+            # Load in optimizer and scheduler states
+            if is_torch_tpu_available():
+                # On TPU we have to take some extra precautions to properly load the states on the right device.
+                optimizer_state = torch.load(os.path.join(model_path, "optimizer.pt"), map_location="cpu")
+                with warnings.catch_warnings(record=True) as caught_warnings:
+                    lr_scheduler_state = torch.load(os.path.join(model_path, "scheduler.pt"), map_location="cpu")
+                reissue_pt_warnings(caught_warnings)
+
+                xm.send_cpu_data_to_device(optimizer_state, self.args.device)
+                xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)
+
+                self.optimizer.load_state_dict(optimizer_state)
+                self.lr_scheduler.load_state_dict(lr_scheduler_state)
+            else:
+                self.optimizer.load_state_dict(
+                    torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
+                )
+                with warnings.catch_warnings(record=True) as caught_warnings:
+                    self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
+                reissue_pt_warnings(caught_warnings)
+
     def hyperparameter_search(
         self,
         hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,

src/transformers/trainer_callback.py

@@ -436,10 +436,12 @@ class ProgressCallback(TrainerCallback):
     def on_train_begin(self, args, state, control, **kwargs):
         if state.is_local_process_zero:
             self.training_bar = tqdm(total=state.max_steps)
+        self.current_step = 0
 
     def on_step_end(self, args, state, control, **kwargs):
         if state.is_local_process_zero:
-            self.training_bar.update(1)
+            self.training_bar.update(state.global_step - self.current_step)
+            self.current_step = state.global_step
 
     def on_prediction_step(self, args, state, control, eval_dataloader=None, **kwargs):
         if state.is_local_process_zero:

src/transformers/trainer_pt_utils.py

@@ -23,6 +23,7 @@ from typing import List, Optional, Union
 
 import numpy as np
 import torch
+from torch.optim.lr_scheduler import SAVE_STATE_WARNING
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import RandomSampler, Sampler
 
@@ -33,8 +34,6 @@ from .utils import logging
 if is_torch_tpu_available():
     import torch_xla.core.xla_model as xm
 
-PT_LR_SCHEDULER_WARNING = "Please also save or load the state of the optimzer when saving or loading the scheduler."
-
 logger = logging.get_logger(__name__)
 
 
@@ -112,10 +111,10 @@ def distributed_broadcast_scalars(
 
 
 def reissue_pt_warnings(caught_warnings):
-    # Reissue warnings that are not the PT_LR_SCHEDULER_WARNING
+    # Reissue warnings that are not the SAVE_STATE_WARNING
     if len(caught_warnings) > 1:
         for w in caught_warnings:
-            if w.category != UserWarning or w.message != PT_LR_SCHEDULER_WARNING:
+            if w.category != UserWarning or w.message != SAVE_STATE_WARNING:
                 warnings.warn(w.message, w.category)