paddle 和测试例跟进 set_dist_repro_dataloader 函数；修改test_trainer_wo_evaluator_torch.py的bug

fastNLP/core/drivers/paddle_driver/single_device.py

@@ -123,17 +123,24 @@ class PaddleSingleDriver(PaddleDriver):
 
         if reproducible:
             if isinstance(args.sampler, paddle.io.RandomSampler):
-                # 如果本来就是随机的，直接替换
-                sampler = RandomSampler(args.sampler.data_source)
-                logger.debug("Replace paddle RandomSampler into fastNLP RandomSampler.")
+                if getattr(args.sampler, '_num_samples', None) is None \
+                        and getattr(args.sampler, 'replacements', False) is False \
+                        and getattr(args.sampler, 'generator', None) is None:
+                    # 如果本来就是随机的，并且没有定制，直接替换掉。
+                    sampler = RandomSampler(args.sampler.data_source, shuffle=True)
+                    logger.debug("Replace paddle RandomSampler into fastNLP RandomSampler.")
+                    return replace_sampler(dataloader, sampler)
+            elif isinstance(args.sampler, paddle.io.SequenceSampler):
+                # 需要替换为不要 shuffle 的。
+                sampler = RandomSampler(args.sampler.data_source, shuffle=False)
+                logger.debug("Replace paddle SequentialSampler into fastNLP RandomSampler.")
                 return replace_sampler(dataloader, sampler)
-            else:
-                batch_sampler = ReproduceBatchSampler(
-                    batch_sampler=args.batch_sampler,
-                    batch_size=args.batch_size,
-                    drop_last=args.drop_last
-                )
-                return replace_batch_sampler(dataloader, batch_sampler)
+            batch_sampler = ReproduceBatchSampler(
+                batch_sampler=args.batch_sampler,
+                batch_size=args.batch_size,
+                drop_last=args.drop_last
+            )
+            return replace_batch_sampler(dataloader, batch_sampler)
         else:
             return dataloader
 

tests/core/controllers/test_trainer_wo_evaluator_torch.py

@@ -250,7 +250,7 @@ def test_trainer_output_from_new_proc(
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", [1, 2])])
+@pytest.mark.parametrize("driver,device", [("torch", [0, 1])])
 @pytest.mark.parametrize("cur_rank", [0])  # 依次测试如果是当前进程出现错误，是否能够正确地 kill 掉其他进程；  , 1, 2, 3
 @magic_argv_env_context
 def test_trainer_on_exception(

tests/core/drivers/paddle_driver/test_single_device.py

@@ -386,22 +386,16 @@ class TestSetDistReproDataloader:
     def test_with_reproducible_true(self, shuffle):
         """
         测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现
-        当dist为字符串时，此时应该返回新的 dataloader，且如果原 sampler 为 paddle.io.RandomSampler（shuffle=True），
-        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 ReproduceBatchSampler
+        当dist为字符串时，此时应该返回新的 dataloader，会替换 sampler 为 RandomSampler
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True)
 
         assert not (replaced_loader is dataloader)
-        if shuffle:
-            # 此时会替换 sampler
-            assert isinstance(replaced_loader.batch_sampler, paddle.io.BatchSampler)
-            assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
-            assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
-        else:
-            # 此时会替换 batch_sampler
-            assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
-            assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler)
+        assert isinstance(replaced_loader.batch_sampler, paddle.io.BatchSampler)
+        assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
+        assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
+        assert replaced_loader.batch_sampler.sampler.shuffle == shuffle
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last
 

tests/core/drivers/torch_driver/test_single_device.py

@@ -400,22 +400,19 @@ class TestSetDistReproDataloader:
     def test_with_reproducible_true(self, shuffle):
         """
         测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现
-        当dist为字符串时，此时应该返回新的 dataloader，且如果原 sampler 为 torch.utils.data.RandomSampler（shuffle=True），
-        只会替换 Sampler 为 RandomSampler；否则会替换 batch_sampler 为 ReproduceBatchSampler
+        当dist为字符串时，此时应该返回新的 dataloader，会替换 sampler 为 RandomSampler；
+        TODO:
+            在 Sampler 的参数不是默认的情况下会替换 batch_sampler 
         """
         dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle)
         replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True)
 
         assert not (replaced_loader is dataloader)
-        if shuffle:
-            # 此时会替换 sampler
-            assert isinstance(replaced_loader.batch_sampler, torch.utils.data.BatchSampler)
-            assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
-            assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
-        else:
-            # 此时会替换 batch_sampler
-            assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler)
-            assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler)
+        # 替换 sampler
+        assert isinstance(replaced_loader.batch_sampler, torch.utils.data.BatchSampler)
+        assert not (replaced_loader.batch_sampler is dataloader.batch_sampler)
+        assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
+        assert replaced_loader.batch_sampler.sampler.shuffle == shuffle
         assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size
         assert replaced_loader.drop_last == dataloader.drop_last