1.修改torch测试例中多卡的driver参数 2.修改测试例中的driver.save driver.load为driver.save_checkpoint driver.load_checkpoint 3.添加lstm

fastNLP/core/drivers/torch_driver/initialize_torch_driver.py

@@ -32,7 +32,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.devi
         return TorchDDPDriver(model, torch.device(f"cuda:{os.environ['LOCAL_RANK']}"), True, **kwargs)
 
     if driver not in {"torch", "fairscale"}:
-        raise ValueError("Parameter `driver` can only be one of these values: ['torch', 'torch_ddp', 'fairscale'].")
+        raise ValueError("Parameter `driver` can only be one of these values: ['torch', 'fairscale'].")
 
     _could_use_device_num = torch.cuda.device_count()
     if isinstance(device, str):
@@ -43,6 +43,7 @@ def initialize_torch_driver(driver: str, device: Optional[Union[str, "torch.devi
                 raise ValueError("Parameter `device` can only be '-1' when it is smaller than 0.")
             device = [torch.device(f"cuda:{w}") for w in range(_could_use_device_num)]
         elif device >= _could_use_device_num:
+            print(device, _could_use_device_num)
             raise ValueError("The gpu device that parameter `device` specifies is not existed.")
         else:
             device = torch.device(f"cuda:{device}")

fastNLP/modules/torch/encoder/__init__.py

@@ -0,0 +1,5 @@
+__all__ = [
+    "LSTM",
+]
+
+from .lstm import LSTM

fastNLP/modules/torch/encoder/lstm.py

@@ -0,0 +1,82 @@
+r"""undocumented
+轻量封装的 Pytorch LSTM 模块.
+可在 forward 时传入序列的长度, 自动对padding做合适的处理.
+"""
+
+__all__ = [
+    "LSTM"
+]
+
+import torch
+import torch.nn as nn
+import torch.nn.utils.rnn as rnn
+
+
+class LSTM(nn.Module):
+    r"""
+    LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
+    为1; 且可以应对DataParallel中LSTM的使用问题。
+    """
+
+    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True,
+                 bidirectional=False, bias=True):
+        r"""
+        
+        :param input_size:  输入 `x` 的特征维度
+        :param hidden_size: 隐状态 `h` 的特征维度. 如果bidirectional为True，则输出的维度会是hidde_size*2
+        :param num_layers: rnn的层数. Default: 1
+        :param dropout: 层间dropout概率. Default: 0
+        :param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False``
+        :param batch_first: 若为 ``True``, 输入和输出 ``Tensor`` 形状为
+            :(batch, seq, feature). Default: ``False``
+        :param bias: 如果为 ``False``, 模型将不会使用bias. Default: ``True``
+        """
+        super(LSTM, self).__init__()
+        self.batch_first = batch_first
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first,
+                            dropout=dropout, bidirectional=bidirectional)
+        self.init_param()
+
+    def init_param(self):
+        for name, param in self.named_parameters():
+            if 'bias' in name:
+                # based on https://github.com/pytorch/pytorch/issues/750#issuecomment-280671871
+                param.data.fill_(0)
+                n = param.size(0)
+                start, end = n // 4, n // 2
+                param.data[start:end].fill_(1)
+            else:
+                nn.init.xavier_uniform_(param)
+
+    def forward(self, x, seq_len=None, h0=None, c0=None):
+        r"""
+        :param x: [batch, seq_len, input_size] 输入序列
+        :param seq_len: [batch, ] 序列长度, 若为 ``None``, 所有输入看做一样长. Default: ``None``
+        :param h0: [batch, hidden_size] 初始隐状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :param c0: [batch, hidden_size] 初始Cell状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :return (output, (ht, ct)): output: [batch, seq_len, hidden_size*num_direction] 输出序列
+            和 ht,ct: [num_layers*num_direction, batch, hidden_size] 最后时刻隐状态.
+        """
+        batch_size, max_len, _ = x.size()
+        if h0 is not None and c0 is not None:
+            hx = (h0, c0)
+        else:
+            hx = None
+        if seq_len is not None and not isinstance(x, rnn.PackedSequence):
+            sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
+            if self.batch_first:
+                x = x[sort_idx]
+            else:
+                x = x[:, sort_idx]
+            x = rnn.pack_padded_sequence(x, sort_lens.cpu(), batch_first=self.batch_first)
+            output, hx = self.lstm(x, hx)  # -> [N,L,C]
+            output, _ = rnn.pad_packed_sequence(output, batch_first=self.batch_first, total_length=max_len)
+            _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)
+            if self.batch_first:
+                output = output[unsort_idx]
+            else:
+                output = output[:, unsort_idx]
+            hx = hx[0][:, unsort_idx], hx[1][:, unsort_idx]
+        else:
+            output, hx = self.lstm(x, hx)
+        return output, hx

tests/core/callbacks/test_checkpoint_callback_torch.py

@@ -74,7 +74,7 @@ def model_and_optimizers(request):
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", [0, 1])])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("version", [0, 1])
 @pytest.mark.parametrize("only_state_dict", [True, False])
 @magic_argv_env_context(timeout=100)
@@ -121,7 +121,7 @@ def test_model_checkpoint_callback_1(
         # 检查生成保存模型文件的数量是不是正确的；
         if version == 0:
 
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "model-epoch_10" in all_saved_model_paths
                 assert "model-epoch_4-batch_123" in all_saved_model_paths
 
@@ -144,7 +144,7 @@ def test_model_checkpoint_callback_1(
 
             pattern = re.compile("model-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "model-epoch_9" in all_saved_model_paths
                 assert "model-last" in all_saved_model_paths
                 aLL_topk_folders = []
@@ -206,7 +206,7 @@ def test_model_checkpoint_callback_1(
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("only_state_dict", [True])
 @magic_argv_env_context(timeout=100)
 def test_model_checkpoint_callback_2(
@@ -259,7 +259,7 @@ def test_model_checkpoint_callback_2(
         # 检查生成保存模型文件的数量是不是正确的；
         all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
 
-        if driver == "torch":
+        if not isinstance(device, list):
             assert "model-epoch_4-batch_100-exception_NotImplementedError" in all_saved_model_paths
             exception_model_path = all_saved_model_paths["model-epoch_4-batch_100-exception_NotImplementedError"]
         # ddp 下的文件名不同，因为同样的数据，ddp 用了更少的步数跑完；
@@ -299,7 +299,7 @@ def test_model_checkpoint_callback_2(
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 0)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 0)])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("version", [0, 1])
 @pytest.mark.parametrize("only_state_dict", [True, False])
 @magic_argv_env_context(timeout=100)
@@ -347,7 +347,7 @@ def test_trainer_checkpoint_callback_1(
         # 检查生成保存模型文件的数量是不是正确的；
         if version == 0:
 
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "trainer-epoch_7" in all_saved_model_paths
                 assert "trainer-epoch_4-batch_123" in all_saved_model_paths
 
@@ -371,7 +371,7 @@ def test_trainer_checkpoint_callback_1(
             pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
             # all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "trainer-last" in all_saved_model_paths
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
@@ -417,7 +417,7 @@ def test_trainer_checkpoint_callback_1(
                 n_epochs=13,
                 output_from_new_proc="all"
             )
-            trainer.load(folder, only_state_dict=only_state_dict)
+            trainer.load_checkpoint(folder, only_state_dict=only_state_dict)
 
             trainer.run()
             trainer.driver.barrier()
@@ -489,7 +489,7 @@ def test_load_state(model_and_optimizers):
             callbacks=callbacks,
             output_from_new_proc="all"
         )
-        trainer.load(folder=epoch_2_path)
+        trainer.load_checkpoint(folder=epoch_2_path)
         with Capturing() as output:
             trainer.run(num_eval_sanity_batch=0, num_train_batch_per_epoch=2)
 
@@ -503,7 +503,7 @@ def test_load_state(model_and_optimizers):
 
 @pytest.mark.torch
 # 通过自己编写 model_save_fn 和 model_load_fn 来测试 huggingface 的 transformers 的模型的保存和加载；
-@pytest.mark.parametrize("driver,device", [("torch_ddp", [6, 7]), ("torch", 7)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", [6, 7]), ("torch", 7)])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("version", [0, 1])
 @magic_argv_env_context
 @pytest.mark.skip("Skip transformers test for now.")
@@ -675,7 +675,7 @@ def test_trainer_checkpoint_callback_2(
         # 检查生成保存模型文件的数量是不是正确的；
         if version == 0:
 
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "trainer-epoch_1-batch_200" in all_saved_model_paths
 
                 epoch_save_path = all_saved_model_paths["trainer-epoch_1-batch_200"]
@@ -695,7 +695,7 @@ def test_trainer_checkpoint_callback_2(
             pattern = re.compile("trainer-epoch_[0-9]+-batch_[0-9]+-[a-zA-Z#]+_[0-9]*.?[0-9]*")
 
             # all_saved_model_paths = {w.name: w for w in path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).iterdir()}
-            if driver == "torch":
+            if not isinstance(device, list):
                 assert "trainer-last" in all_saved_model_paths
                 aLL_topk_folders = []
                 for each_folder_name in all_saved_model_paths:
@@ -740,7 +740,7 @@ def test_trainer_checkpoint_callback_2(
                 output_mapping=bert_output_mapping,
                 metrics={"acc": acc},
             )
-            trainer.load(folder, model_load_fn=model_load_fn)
+            trainer.load_checkpoint(folder, model_load_fn=model_load_fn)
 
             trainer.run()
             trainer.driver.barrier()

tests/core/callbacks/test_load_best_model_callback_torch.py

@@ -72,7 +72,7 @@ def model_and_optimizers(request):
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch_ddp", [4, 5]), ("torch", 1), ("torch", "cpu")])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", [4, 5]), ("torch", 1), ("torch", "cpu")])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("save_folder", ['save_models', None])
 @pytest.mark.parametrize("only_state_dict", [True, False])
 @magic_argv_env_context

tests/core/callbacks/test_more_evaluate_callback.py

@@ -98,7 +98,7 @@ def model_and_optimizers(request):
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("version", [0, 1])
 @pytest.mark.parametrize("only_state_dict", [True, False])
 @magic_argv_env_context
@@ -183,7 +183,7 @@ def test_model_more_evaluate_callback_1(
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 0)])  # ("torch", "cpu"), ("torch_ddp", [0, 1]), ("torch", 1)
+@pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", [0, 1]), ("torch", 0)])  # ("torch", "cpu"), ("torch", [0, 1]), ("torch", 1)
 @pytest.mark.parametrize("version", [0, 1])
 @pytest.mark.parametrize("only_state_dict", [True, False])
 @magic_argv_env_context
@@ -256,7 +256,7 @@ def test_trainer_checkpoint_callback_1(
                 evaluate_fn='train_step'
             )
             folder = path.joinpath(os.environ[FASTNLP_LAUNCH_TIME]).joinpath(folder)
-            trainer.load(folder, only_state_dict=only_state_dict)
+            trainer.load_checkpoint(folder, only_state_dict=only_state_dict)
 
             trainer.run()
             trainer.driver.barrier()

tests/core/controllers/_test_distributed_launch_torch_1.py

@@ -85,7 +85,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps(
 ):
     trainer = Trainer(
         model=model,
-        driver="torch_ddp",
+        driver="torch",
         device=None,
         optimizers=optimizers,
         train_dataloader=train_dataloader,

tests/core/controllers/_test_distributed_launch_torch_2.py

@@ -73,7 +73,7 @@ def _test_trainer_torch_with_evaluator_fp16_accumulation_steps(
 ):
     trainer = Trainer(
         model=model,
-        driver="torch_ddp",
+        driver="torch",
         device=None,
         optimizers=optimizers,
         train_dataloader=train_dataloader,

tests/core/controllers/test_trainer_wo_evaluator_torch.py

@@ -318,7 +318,7 @@ def test_torch_distributed_launch_2(version):
 
 
 @pytest.mark.torch
-@pytest.mark.parametrize("driver,device", [("torch", 0), ("torch_ddp", [0, 1])])
+@pytest.mark.parametrize("driver,device", [("torch", 0), ("torch", [0, 1])])
 @magic_argv_env_context
 def test_torch_wo_auto_param_call(
     driver,

tests/core/drivers/paddle_driver/test_fleet.py

@@ -626,9 +626,9 @@ class TestSaveLoad:
             sampler_states = dataloader.batch_sampler.state_dict()
             save_states = {"num_consumed_batches": num_consumed_batches}
             if only_state_dict:
-                self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+                self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
             else:
-                self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
+                self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
             # 加载
             # 更改 batch_size
             dataloader = DataLoader(
@@ -644,7 +644,7 @@ class TestSaveLoad:
                 rank=self.driver2.global_rank,
                 pad=True
             )
-            load_states = self.driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+            load_states = self.driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
             replaced_loader = load_states.pop("dataloader")
             # 1. 检查 optimizer 的状态
             # TODO optimizer 的 state_dict 总是为空
@@ -736,9 +736,9 @@ class TestSaveLoad:
             sampler_states = dataloader.batch_sampler.sampler.state_dict()
             save_states = {"num_consumed_batches": num_consumed_batches}
             if only_state_dict:
-                self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+                self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
             else:
-                self.driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
+                self.driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
             # 加载
             # 更改 batch_size
             batch_sampler = BatchSampler(dataset=self.dataset, batch_size=2)
@@ -752,7 +752,7 @@ class TestSaveLoad:
                 self.dataset,
                 batch_sampler=batch_sampler
             )
-            load_states = self.driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+            load_states = self.driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
             replaced_loader = load_states.pop("dataloader")
 
             # 1. 检查 optimizer 的状态

tests/core/drivers/paddle_driver/test_single_device.py

@@ -615,16 +615,16 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
         sampler_states = dataloader.batch_sampler.state_dict()
         save_states = {"num_consumed_batches": num_consumed_batches}
         if only_state_dict:
-            driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
         else:
-            driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
         # 加载
         # 更改 batch_size
         dataloader = DataLoader(
             dataset=dataset,
             batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False)
         )
-        load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+        load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
         replaced_loader = load_states.pop("dataloader")
         # 1. 检查 optimizer 的状态
         # TODO optimizer 的 state_dict 总是为空
@@ -697,9 +697,9 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16):
         sampler_states = dataloader.batch_sampler.sampler.state_dict()
         save_states = {"num_consumed_batches": num_consumed_batches}
         if only_state_dict:
-            driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
         else:
-            driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[paddle.ones((16, 10))])
         
         # 加载
         # 更改 batch_size
@@ -709,7 +709,7 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16):
             dataset,
             batch_sampler=batch_sampler
         )
-        load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+        load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
         replaced_loader = load_states.pop("dataloader")
 
         # 1. 检查 optimizer 的状态

tests/core/drivers/torch_driver/test_ddp.py

@@ -648,7 +648,7 @@ class TestSaveLoad:
             # 保存状态
             sampler_states = dataloader.batch_sampler.state_dict()
             save_states = {"num_consumed_batches": num_consumed_batches}
-            driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
             # 加载
             # 更改 batch_size
             dataloader = dataloader_with_bucketedbatchsampler(
@@ -663,7 +663,7 @@ class TestSaveLoad:
                 rank=driver2.global_rank,
                 pad=True
             )
-            load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+            load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
             replaced_loader = load_states.pop("dataloader")
             # 1. 检查 optimizer 的状态
             # TODO optimizer 的 state_dict 总是为空
@@ -754,9 +754,9 @@ class TestSaveLoad:
             sampler_states = dataloader.batch_sampler.sampler.state_dict()
             save_states = {"num_consumed_batches": num_consumed_batches}
             if only_state_dict:
-                driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+                driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
             else:
-                driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[torch.ones((16, 10))])
+                driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[torch.ones((16, 10))])
             # 加载
             # 更改 batch_size
             dataloader = dataloader_with_randomsampler(self.dataset, 2, True, False, unrepeated=False)
@@ -765,7 +765,7 @@ class TestSaveLoad:
                 rank=driver2.global_rank,
                 pad=True
             )
-            load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+            load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
             replaced_loader = load_states.pop("dataloader")
 
             # 1. 检查 optimizer 的状态

tests/core/drivers/torch_driver/test_initialize_torch_driver.py

@@ -37,28 +37,6 @@ def test_get_single_device(driver, device):
     driver = initialize_torch_driver(driver, device, model)
     assert isinstance(driver, TorchSingleDriver)
 
-
-@pytest.mark.torch
-@pytest.mark.parametrize(
-    "device", 
-    [0, 1]
-)
-@pytest.mark.parametrize(
-    "driver", 
-    ["torch_ddp"]
-)
-@magic_argv_env_context
-def test_get_ddp_2(driver, device):
-    """
-    测试 ddp 多卡的初始化情况，但传入了单个 gpu
-    """
-
-    model = TorchNormalModel_Classification_1(64, 10)
-    driver = initialize_torch_driver(driver, device, model)
-
-    assert isinstance(driver, TorchDDPDriver)
-
-
 @pytest.mark.torch
 @pytest.mark.parametrize(
     "device", 
@@ -66,7 +44,7 @@ def test_get_ddp_2(driver, device):
 )
 @pytest.mark.parametrize(
     "driver", 
-    ["torch", "torch_ddp"]
+    ["torch"]
 )
 @magic_argv_env_context
 def test_get_ddp(driver, device):
@@ -79,21 +57,6 @@ def test_get_ddp(driver, device):
 
     assert isinstance(driver, TorchDDPDriver)
 
-
-@pytest.mark.torch
-@pytest.mark.parametrize(
-    ("driver", "device"), 
-    [("torch_ddp", "cpu")]
-)
-def test_get_ddp_cpu(driver, device):
-    """
-    测试试图在 cpu 上初始化分布式训练的情况
-    """
-    model = TorchNormalModel_Classification_1(64, 10)
-    with pytest.raises(ValueError):
-        driver = initialize_torch_driver(driver, device, model)
-
-
 @pytest.mark.torch
 @pytest.mark.parametrize(
     "device", 
@@ -101,7 +64,7 @@ def test_get_ddp_cpu(driver, device):
 )
 @pytest.mark.parametrize(
     "driver", 
-    ["torch", "torch_ddp"]
+    ["torch"]
 )
 def test_device_out_of_range(driver, device):
     """

tests/core/drivers/torch_driver/test_single_device.py

@@ -595,12 +595,12 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
 
         sampler_states = dataloader.batch_sampler.state_dict()
         save_states = {"num_consumed_batches": num_consumed_batches}
-        driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+        driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
         # 加载
         # 更改 batch_size
 
         dataloader = dataloader_with_randombatchsampler(dataset, 2, True, False)
-        load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+        load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
         replaced_loader = load_states.pop("dataloader")
         # 1. 检查 optimizer 的状态
         # TODO optimizer 的 state_dict 总是为空
@@ -664,12 +664,12 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16):
 
         sampler_states = dataloader.batch_sampler.sampler.state_dict()
         save_states = {"num_consumed_batches": num_consumed_batches}
-        driver1.save(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+        driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
         
         # 加载
         # 更改 batch_size
         dataloader = dataloader_with_randomsampler(dataset, 2, True, False)
-        load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True)
+        load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
         replaced_loader = load_states.pop("dataloader")
 
         # 1. 检查 optimizer 的状态

tests/embeddings/torch/test_char_embedding.py

@@ -7,8 +7,9 @@ from fastNLP import Vocabulary, DataSet, Instance
 from fastNLP.embeddings.torch.char_embedding import LSTMCharEmbedding, CNNCharEmbedding
 
 
+@pytest.mark.torch
 class TestCharEmbed:
-    @pytest.mark.test
+    # @pytest.mark.test
     def test_case_1(self):
         ds = DataSet([Instance(words=['hello', 'world']), Instance(words=['Jack'])])
         vocab = Vocabulary().from_dataset(ds, field_name='words')
@@ -18,7 +19,7 @@ class TestCharEmbed:
         y = embed(x)
         assert tuple(y.size()) == (2, 3, 3)
 
-    @pytest.mark.test
+    # @pytest.mark.test
     def test_case_2(self):
         ds = DataSet([Instance(words=['hello', 'world']), Instance(words=['Jack'])])
         vocab = Vocabulary().from_dataset(ds, field_name='words')