remove device in batch

fastNLP/core/batch.py

@@ -20,8 +20,7 @@ class Batch(object):
     :param str or torch.device device: the batch's device, if as_numpy is True, device is ignored.
     """
 
-    def __init__(self, dataset, batch_size, sampler=RandomSampler(), as_numpy=False, prefetch=False,
-                 device='cpu'):
+    def __init__(self, dataset, batch_size, sampler=RandomSampler(), as_numpy=False, prefetch=False):
         self.dataset = dataset
         self.batch_size = batch_size
         self.sampler = sampler
@@ -32,8 +31,6 @@ class Batch(object):
         self.cur_batch_indices = None
         self.prefetch = prefetch
         self.lengths = 0
-        if not as_numpy:
-            self.device = device if isinstance(device, torch.device) else torch.device(device)
 
     def fetch_one(self):
         if self.curidx >= len(self.idx_list):
@@ -50,7 +47,6 @@ class Batch(object):
                     batch = field.get(indices)
                     if not self.as_numpy and field.padder is not None:
                         batch = to_tensor(batch, field.dtype)
-                        batch = batch.to(self.device)
                     if field.is_target:
                         batch_y[field_name] = batch
                     if field.is_input:
@@ -119,12 +115,18 @@ def run_batch_iter(batch):
     fetch_p.start()
     # print('fork fetch process')
     while 1:
-        res = q.get()
-        q.task_done()
-        # print('get fetched')
-        if res is None:
-            break
-        yield res
+        try:
+            res = q.get(timeout=1)
+            q.task_done()
+            # print('get fetched')
+            if res is None:
+                break
+            yield res
+        except Exception as e:
+            if fetch_p.is_alive():
+                continue
+            else:
+                break
     fetch_p.terminate()
     fetch_p.join()
     # print('iter done')

fastNLP/core/trainer.py

@@ -229,12 +229,13 @@ class Trainer(object):
         with inner_tqdm(total=total_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
             avg_loss = 0
             data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
-                                  prefetch=self.prefetch, device=self._model_device)
+                                  prefetch=self.prefetch)
             for epoch in range(1, self.n_epochs+1):
                 pbar.set_description_str(desc="Epoch {}/{}".format(epoch, self.n_epochs))
                 # early stopping
                 self.callback_manager.before_epoch(epoch, self.n_epochs)
                 for batch_x, batch_y in data_iterator:
+                    _move_dict_value_to_device(batch_x, batch_y, device=self._model_device)
                     indices = data_iterator.get_batch_indices()
                     # negative sampling; replace unknown; re-weight batch_y
                     self.callback_manager.before_batch(batch_x, batch_y, indices)