修改paddle fdl测试用例冲突

fastNLP/core/callbacks/callback_events.py

@@ -4,8 +4,6 @@ from types import DynamicClassAttribute
 from functools import wraps
 
 
-import fastNLP
-
 __all__ = [
     'Events',
     'EventsList',

fastNLP/core/callbacks/callback_manager.py

@@ -11,6 +11,7 @@ from .callback import Callback
 from fastNLP.core.log import logger
 from .progress_callback import ProgressCallback, choose_progress_callback
 from fastNLP.envs import rank_zero_call
+from fastNLP.core.utils.utils import _get_fun_msg
 
 
 def _transfer(func):
@@ -21,10 +22,12 @@ def _transfer(func):
 
     def wrapper(manager, *arg, **kwargs):
         manager.callback_counter[func.__name__] += 1  # 给实际被调用的 callback_fn 的计数加 1；
-        returns = []
         for callback_fn in manager.callback_fns[func.__name__]:
-            returns.append(callback_fn(*arg, **kwargs))
-        return returns
+            try:
+                callback_fn(*arg, **kwargs)
+            except BaseException as e:
+                logger.error(f"The following callback_fn raise exception:{_get_fun_msg(callback_fn)}.")
+                raise e
     return wrapper
 
 

fastNLP/core/collators/new_collator.py

@@ -16,7 +16,7 @@ SUPPORTED_BACKENDS = ['torch', 'jittor', 'paddle', 'numpy', 'raw', 'auto', None]
 CHECK_BACKEND = ['torch', 'jittor', 'paddle']  # backend 为 auto 时 检查是否是这些 backend
 
 
-def _get_backend():
+def _get_backend() -> str:
     """
     当 Collator 的 backend 为 None 的时候如何，通过这个函数自动判定其 backend 。判断方法主要为以下两个：
     （1）尝试通过向上寻找当前 collator 的 callee 对象，根据 callee 对象寻找。然后使用 '/site-packages/{backend}' 来寻找是否是
@@ -57,7 +57,7 @@ def _get_backend():
         else:
             break
     if len(catch_backend):
-        logger.debug(f"Find a file named:{catch_backend[1]} from stack contain backend:{catch_backend[0]}.")
+        logger.debug(f"Find a file named:{catch_backend[1]} from stack contains backend:{catch_backend[0]}.")
         return catch_backend[0]
 
     # 方式 (2)
@@ -66,7 +66,7 @@ def _get_backend():
         if catch_backend:
             break
     if len(catch_backend):
-        logger.debug(f"Find a file named:{catch_backend[1]} from sys.modules contain backend:{catch_backend[0]}.")
+        logger.debug(f"Find a file named:{catch_backend[1]} from sys.modules contains backend:{catch_backend[0]}.")
         return catch_backend[0]
 
     return 'numpy'
@@ -80,7 +80,7 @@ class Collator:
             时候自动根据设置以及数据情况，为每个 field 获取一个 padder ，在之后的每次调用中，都将使用对应的 Padder 给对应的 field 。
 
         :param backend: 对于可以 pad 的 field，使用哪种 tensor，支持 ['torch','jittor','paddle','numpy','raw', auto, None]。
-            若为 'auto' ，则在进行 pad 的时候会根据调用的环境决定其 backend 。该参数对本身就不能进行 pad 的数据没用影响，不能 pad
+            若为 'auto' ，则在进行 pad 的时候会根据调用的环境决定其 backend 。该参数对不能进行 pad 的数据没用影响，不能 pad
             的数据返回一定是 list 。
         """
         self.unpack_batch_func = None
@@ -144,15 +144,18 @@ class Collator:
             for key in unpack_batch.keys():
                 if key not in self.input_fields and key not in self.ignore_fields:
                     self.input_fields[key] = {'pad_val': 0, 'dtype': None, 'backend': self.backend}
+                elif key in self.input_fields and self.input_fields[key]['backend'] == 'auto':
+                    self.input_fields[key]['backend'] = self.backend
 
             for field_name, setting in self.input_fields.items():
                 pad_fn = setting.get('pad_fn', None)
                 if callable(pad_fn):
                     padder = pad_fn
                 else:
+                    backend = self.backend if setting['backend'] == 'auto' else setting['backend']
                     batch_field = unpack_batch.get(field_name)
                     padder = get_padder(batch_field=batch_field, pad_val=setting['pad_val'],
-                                        dtype=setting['dtype'], backend=setting['backend'],
+                                        dtype=setting['dtype'], backend=backend,
                                         field_name=field_name)
                 self.padders[field_name] = padder
             if self.batch_data_type == 'l':

tests/core/controllers/test_trainer_paddle.py

@@ -4,14 +4,15 @@ from dataclasses import dataclass
 from fastNLP.core.controllers.trainer import Trainer
 from fastNLP.core.metrics.accuracy import Accuracy
 from fastNLP.core.callbacks.progress_callback import RichCallback
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
 
-from paddle.optimizer import Adam
-from paddle.io import DataLoader
+if _NEED_IMPORT_PADDLE:
+    from paddle.optimizer import Adam
+    from paddle.io import DataLoader
 
 
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.datasets.paddle_data import PaddleRandomMaxDataset
-from tests.helpers.callbacks.helper_callbacks import RecordLossCallback, RecordMetricCallback
 from tests.helpers.utils import magic_argv_env_context
 
 @dataclass

tests/core/controllers/test_trainer_w_evaluator_torch.py

@@ -100,17 +100,16 @@ def model_and_optimizers(request):
 # 测试一下普通的情况；
 @pytest.mark.torch
 @pytest.mark.parametrize("driver,device", [("torch", "cpu"), ("torch", 1), ("torch", [0, 1])])  #  ("torch", "cpu"), ("torch", 1), ("torch", [0, 1])
-@pytest.mark.parametrize("callbacks", [[RecordMetricCallback(monitor="acc", metric_threshold=0.2, larger_better=True)]])
 @pytest.mark.parametrize("evaluate_every", [-3, -1, 100])
 @magic_argv_env_context
 def test_trainer_torch_with_evaluator(
         model_and_optimizers: TrainerParameters,
         driver,
         device,
-        callbacks,
         evaluate_every,
         n_epochs=10,
 ):
+    callbacks = [RecordMetricCallback(monitor="acc", metric_threshold=0.2, larger_better=True)]
     trainer = Trainer(
         model=model_and_optimizers.model,
         driver=driver,
@@ -172,7 +171,7 @@ def test_trainer_torch_with_evaluator_fp16_accumulation_steps(
     if dist.is_initialized():
         dist.destroy_process_group()
 
-
+@pytest.mark.torch
 @pytest.mark.parametrize("driver,device", [("torch", 1)])  # ("torch", [0, 1]),("torch", 1)
 @magic_argv_env_context
 def test_trainer_validate_every(
@@ -184,9 +183,7 @@ def test_trainer_validate_every(
 
     def validate_every(trainer):
         if trainer.global_forward_batches % 10 == 0:
-            print(trainer)
             print("\nfastNLP test validate every.\n")
-            print(trainer.global_forward_batches)
             return True
 
     trainer = Trainer(

tests/core/dataloaders/paddle_dataloader/test_fdl.py

@@ -1,11 +1,17 @@
 import pytest
+import numpy as np
 
 from fastNLP.core.dataloaders.paddle_dataloader.fdl import PaddleDataLoader
 from fastNLP.core.dataset import DataSet
 from fastNLP.core.log import logger
-from paddle.io import Dataset, DataLoader
-import numpy as np
-import paddle
+
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    from paddle.io import Dataset, DataLoader
+    import paddle
+else:
+    from fastNLP.core.utils.dummy_class import DummyClass as Dataset
+
 
 
 class RandomDataset(Dataset):

tests/core/drivers/paddle_driver/test_dist_utils.py

@@ -14,9 +14,10 @@ from fastNLP.core.drivers.paddle_driver.dist_utils import (
 )
 from fastNLP.core.drivers.paddle_driver.fleet_launcher import FleetLauncher
 from tests.helpers.utils import magic_argv_env_context
-
-import paddle
-import paddle.distributed as dist
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    import paddle.distributed as dist
 
 @pytest.mark.paddle
 class TestDistUtilsTools:

tests/core/drivers/paddle_driver/test_fleet.py

@@ -13,10 +13,11 @@ from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset
 from tests.helpers.utils import magic_argv_env_context
 from fastNLP.core import rank_zero_rm
-
-import paddle
-import paddle.distributed as dist
-from paddle.io import DataLoader, BatchSampler
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    import paddle.distributed as dist
+    from paddle.io import DataLoader, BatchSampler
 
 def generate_driver(num_labels, feature_dimension, device=[0,1], fp16=False, output_from_new_proc="only_error"):
     paddle_model = PaddleNormalModel_Classification_1(num_labels, feature_dimension)

tests/core/drivers/paddle_driver/test_initialize_paddle_driver.py

@@ -5,8 +5,9 @@ from fastNLP.core.drivers.paddle_driver.initialize_paddle_driver import initiali
 from fastNLP.envs import get_gpu_count
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from tests.helpers.utils import magic_argv_env_context
-
-import paddle
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
 
 @pytest.mark.paddle
 def test_incorrect_driver():

tests/core/drivers/paddle_driver/test_single_device.py

@@ -8,10 +8,12 @@ from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandom
 from tests.helpers.datasets.torch_data import TorchNormalDataset
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
 from fastNLP.core import rank_zero_rm
-
-import paddle
-from paddle.io import DataLoader, BatchSampler
-import torch
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    from paddle.io import DataLoader, BatchSampler
+if _NEED_IMPORT_TORCH:
+    import torch
 
 ############################################################################
 #

tests/core/drivers/paddle_driver/test_utils.py

@@ -7,9 +7,10 @@ from fastNLP.core.drivers.paddle_driver.utils import (
     replace_sampler,
 )
 from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
-
-import paddle
-from paddle.io import DataLoader, BatchSampler
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    from paddle.io import DataLoader, BatchSampler
 
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset
 

tests/core/drivers/torch_driver/test_single_device.py

@@ -8,10 +8,12 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDat
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from fastNLP.core import rank_zero_rm
-
-import torch
-from torch.utils.data import DataLoader, BatchSampler
-import paddle
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE, _NEED_IMPORT_TORCH
+if _NEED_IMPORT_TORCH:
+    import torch
+    from torch.utils.data import DataLoader, BatchSampler
+if _NEED_IMPORT_PADDLE:
+    import paddle
 
 def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last):
     """

tests/core/metrics/test_accutacy_paddle.py → tests/core/metrics/test_accuracy_paddle.py

@@ -1,12 +1,14 @@
 import os
 
 import pytest
-import paddle
-import paddle.distributed
-import paddle.distributed.fleet.base.role_maker as role_maker
-import paddle.distributed.fleet as fleet
 from fastNLP.core.metrics import Accuracy
 from fastNLP.core.drivers.paddle_driver.fleet_launcher import FleetLauncher
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    import paddle.distributed
+    import paddle.distributed.fleet.base.role_maker as role_maker
+    import paddle.distributed.fleet as fleet
 
 
 ############################################################################

tests/core/utils/test_paddle_utils.py

@@ -1,7 +1,9 @@
 import pytest
-import paddle
 
 from fastNLP.core.utils.paddle_utils import paddle_to, paddle_move_data_to_device
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
 
 
 ############################################################################

tests/helpers/callbacks/helper_callbacks.py

@@ -36,12 +36,10 @@ class RecordMetricCallback(Callback):
         self.larger_better = larger_better
         self.metric = None
         self.metric_threshold = metric_threshold
-        self.metric_begin_value = None
+        self.metric_begin_value = float('-inf') if larger_better else float('inf')
 
     def on_evaluate_end(self, trainer, results):
         self.metric = results[self.monitor]
-        if self.metric_begin_value is None:
-            self.metric_begin_value = self.metric
 
     def on_train_end(self, trainer):
         if self.larger_better:

tests/helpers/datasets/paddle_data.py

@@ -1,7 +1,12 @@
-import paddle
-from paddle.io import Dataset
 import numpy as np
 
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    from paddle.io import Dataset
+else:
+    from fastNLP.core.utils.dummy_class import DummyClass as Dataset
+
 
 class PaddleNormalDataset(Dataset):
     def __init__(self, num_of_data=1000):

tests/helpers/models/paddle_model.py

@@ -1,7 +1,12 @@
-import paddle
-import paddle.nn as nn
-
-class PaddleNormalModel_Classification_1(paddle.nn.Layer):
+from fastNLP.envs.imports import _NEED_IMPORT_PADDLE
+if _NEED_IMPORT_PADDLE:
+    import paddle
+    import paddle.nn as nn
+    from paddle.nn import Layer
+else:
+    from fastNLP.core.utils.dummy_class import DummyClass as Layer
+
+class PaddleNormalModel_Classification_1(Layer):
     """
     基础的paddle分类模型
     """
@@ -32,7 +37,7 @@ class PaddleNormalModel_Classification_1(paddle.nn.Layer):
         return {"pred": x, "target": y.reshape((-1,))}
 
 
-class PaddleNormalModel_Classification_2(paddle.nn.Layer):
+class PaddleNormalModel_Classification_2(Layer):
     """
     基础的paddle分类模型，只实现 forward 函数测试用户自己初始化了分布式的场景
     """

tests/helpers/utils.py

@@ -30,12 +30,12 @@ def recover_logger(fn):
     return wrapper
 
 
-def magic_argv_env_context(fn=None, timeout=600):
+def magic_argv_env_context(fn=None, timeout=300):
     """
     用来在测试时包裹每一个单独的测试函数，使得 ddp 测试正确；
     会丢掉 pytest 中的 arg 参数。
 
-    :param timeout: 表示一个测试如果经过多久还没有通过的话就主动将其 kill 掉，默认为 10 分钟，单位为秒；
+    :param timeout: 表示一个测试如果经过多久还没有通过的话就主动将其 kill 掉，默认为 5 分钟，单位为秒；
     :return:
     """
     # 说明是通过 @magic_argv_env_context(timeout=600) 调用；