修复mixdataloader在torch1.6下参数不匹配的问题

fastNLP/core/dataloaders/torch_dataloader/mix_dataloader.py

@@ -5,6 +5,7 @@ __all__ = [
 from typing import Optional, Callable, List, Union, Tuple, Dict, Sequence, Mapping
 
 import numpy as np
+from pkg_resources import parse_version
 
 from fastNLP.core.dataset import DataSet, Instance
 from fastNLP.core.samplers import PollingSampler, MixSequentialSampler, DopedSampler
@@ -12,6 +13,7 @@ from fastNLP.envs.imports import _NEED_IMPORT_TORCH
 from fastNLP.core.collators import Collator
 
 if _NEED_IMPORT_TORCH:
+    from torch import __version__ as torchversion
     from torch.utils.data import DataLoader, Sampler
 else:
     from fastNLP.core.utils.dummy_class import DummyClass as DataLoader
@@ -213,13 +215,21 @@ class MixDataLoader(DataLoader):
         else:
             raise ValueError(f"{mode} must be sequential, polling, mix or batch_sampler")
 
-        super(MixDataLoader, self).__init__(
-            _MixDataset(datasets=dataset), batch_size=1, shuffle=False, sampler=None,
-            batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=collate_fn,
-            pin_memory=pin_memory, drop_last=False, timeout=0,
-            worker_init_fn=None, multiprocessing_context=None, generator=None,
-            prefetch_factor=2, persistent_workers=False
-        )
+        if parse_version(torchversion) >= parse_version('1.7'):
+            super(MixDataLoader, self).__init__(
+                _MixDataset(datasets=dataset), batch_size=1, shuffle=False, sampler=None,
+                batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=collate_fn,
+                pin_memory=pin_memory, drop_last=False, timeout=0,
+                worker_init_fn=None, multiprocessing_context=None, generator=None,
+                prefetch_factor=2, persistent_workers=False
+            )
+        else:
+            super(MixDataLoader, self).__init__(
+                _MixDataset(datasets=dataset), batch_size=1, shuffle=False, sampler=None,
+                batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=collate_fn,
+                pin_memory=pin_memory, drop_last=False, timeout=0,
+                worker_init_fn=None, multiprocessing_context=None, generator=None,
+            )
 
     def __iter__(self):
         return super().__iter__()