1.修复yxg同学发现的DataSet copy Field导致的bug; 2. 修改DistTrainer中update_every的bug; 3.Trainer和DistTrainer修改pin_memory默认值为True

fastNLP/core/dataset.py

@@ -474,8 +474,8 @@ class DataSet(object):
             if idx.start is not None and (idx.start >= len(self) or idx.start <= -len(self)):
                 raise RuntimeError(f"Start index {idx.start} out of range 0-{len(self) - 1}")
             data_set = DataSet()
-            for field in self.field_arrays.values():
-                data_set.add_field(field_name=field.name, fields=field.content[idx], padder=field.padder,
+            for field_name, field in self.field_arrays.items():
+                data_set.add_field(field_name=field_name, fields=field.content[idx], padder=field.padder,
                                    is_input=field.is_input, is_target=field.is_target, ignore_type=field.ignore_type)
             data_set.collater = self.collater.copy_from(self.collater)
             return data_set
@@ -616,6 +616,7 @@ class DataSet(object):
         if len(self) != len(fieldarray):
             raise RuntimeError(f"The field to add must have the same size as dataset. "
                                f"Dataset size {len(self)} != field size {len(fieldarray)}")
+        fieldarray.name = field_name
         self.field_arrays[field_name] = fieldarray
 
     def add_field(self, field_name, fields, padder=AutoPadder(), is_input=False, is_target=False, ignore_type=False):
@@ -673,6 +674,7 @@ class DataSet(object):
         if not self.has_field(field_name):
             raise KeyError(f"Field:{field_name} not found in DataSet.")
         fieldarray = deepcopy(self.get_field(field_name))
+        fieldarray.name = new_field_name
         self.add_fieldarray(field_name=new_field_name, fieldarray=fieldarray)
         return self
 

fastNLP/core/dist_trainer.py

@@ -55,7 +55,7 @@ def get_local_rank():
     raise RuntimeError('Please use "python -m torch.distributed.launch --nproc_per_node=N train_script.py')
 
 
-class DistTrainer():
+class DistTrainer:
     r"""
     分布式的 Trainer，支持分布式训练和混合精度的训练。具体实现原理请阅读 pytorch 官方文档。
 
@@ -110,7 +110,7 @@ class DistTrainer():
             int dev_batch_size: 在evaluate时，使用的evaluate的batch大小
             bool test_use_fp16: test时使用fp16
             bool set_grad_to_none: zero_grad时将grad设为None而不是0
-            GradScaler gradscaler: 自定义的梯度 scaler
+            GradScaler grad_scaler: 自定义的梯度 scaler
             bool pin_memory: 是否将产生的tensor使用pin memory, 可能会加快数据速度。一般在tensor较多或tensor维度较大时，有速度增益。
             bool find_unused_parameters: 在将model转化为DistributedDataParallel类型的时候，需要填入该参数，除非model内确实有
                 forward没用上的参数，否则应该不需要用到该参数。
@@ -132,6 +132,7 @@ class DistTrainer():
         self.rank = dist.get_rank() # unique id for each process
 
         self.train_data = train_data
+        self.kwargs = kwargs
         if kwargs.get('batch_size', None):
             batch_size_per_gpu = int(kwargs.get('batch_size'))
         self.batch_size_per_gpu = int(batch_size_per_gpu)
@@ -158,15 +159,15 @@ class DistTrainer():
         # init fp16, must before DataParallel init
         autocast, GradScaler = _build_fp16_env(dummy=not self.fp16)
         self.auto_cast = autocast
-        user_grad_scaler = getattr(kwargs, 'gradscaler', None)
+        user_grad_scaler = kwargs.get('grad_scaler', None)
         if user_grad_scaler is not None:
-            assert self.fp16, "must set fp16=True to enable gradscaler"
+            assert self.fp16, "must set fp16=True to enable grad_scaler"
             grad_scaler = user_grad_scaler
         else:
             grad_scaler = GradScaler()
         self.grad_scaler = grad_scaler
 
-        self.set_grad_to_none = getattr(kwargs, 'set_grad_to_none', True)
+        self.set_grad_to_none = kwargs.get('set_grad_to_none', False)
 
         # init DataParallel
         if parse_version(torch.__version__)>=parse_version('1.1'):
@@ -191,7 +192,8 @@ class DistTrainer():
                 elif hasattr(sampler, 'set_batch_size'):
                     sampler.set_batch_size(batch_size_per_gpu)
                 self.sampler = sampler
-        self.pin_memory = kwargs.get('pin_memory', True)
+        # concerning issue from https://github.com/pytorch/pytorch/issues/57273
+        self.pin_memory = kwargs.get('pin_memory', False if parse_version(torch.__version__)==parse_version('1.9') else True)
         self.data_iterator = self._get_data_iter(self.train_data)
         self.batch_size = self.world_size * self.batch_size_per_gpu
         self.n_steps = self._get_n_steps()
@@ -199,7 +201,6 @@ class DistTrainer():
         self.dev_data = dev_data
         self.metrics = metrics
         self.test_use_tqdm = True
-        self.kwargs = kwargs
         self.test_use_tqdm = kwargs.get('test_use_tqdm', self.use_tqdm)
         dev_batch_size = kwargs.get('dev_batch_size', batch_size_per_gpu)
 
@@ -229,22 +230,6 @@ class DistTrainer():
         self.logger.info("Num of processes: {}".format(self.world_size))
         self.logger.info("Use device: {}".format(device))
 
-    def _maybe_no_sync(self):
-        """
-        Whenever *samples* contains more than one mini-batch, we
-        want to accumulate gradients locally and only call
-        all-reduce in the last backwards pass.
-        """
-        i = self.step % self.update_every
-        if (
-                self.world_size > 1
-                and hasattr(self.ddp_model, "no_sync")
-                and i != 0
-        ):
-            return self.ddp_model.no_sync()
-        else:
-            return contextlib.ExitStack()  # dummy contextmanager
-
     def _get_n_steps(self):
         return len(self.data_iterator) * self.n_epochs
 
@@ -365,37 +350,42 @@ class DistTrainer():
             self.callback_manager.on_epoch_begin()
             for batch_x, batch_y in data_iterator:
                 self.step += 1
-                self.ddp_model.train()
-                _move_dict_value_to_device(batch_x, batch_y, device=self.device, non_blocking=self.pin_memory)
-                indices = data_iterator.get_batch_indices()
-                # negative sampling; replace unknown; re-weight batch_y
-                self.callback_manager.on_batch_begin(batch_x, batch_y, indices)
-                with self.auto_cast():
-                    prediction = self._data_forward(self.ddp_model, batch_x)
-                    # edit prediction
-                    self.callback_manager.on_loss_begin(batch_y, prediction)
-                    loss = self._compute_loss(prediction, batch_y)
-
-                avg_loss += loss.detach()
-
-                # Is loss NaN or inf? requires_grad = False
-                self.callback_manager.on_backward_begin(loss)
-                self._grad_backward(loss)
-                self.callback_manager.on_backward_end()
-                self._update()
-                self.callback_manager.on_step_end()
-
-                if self.step % self.print_every == 0:
-                    avg_loss = float(avg_loss) / self.print_every
-                    print_output = "loss:{:<6.5f}".format(avg_loss)
-                    pbar.update(self.print_every)
-                    pbar.set_postfix_str(print_output)
-                    avg_loss = 0
-
-                self.callback_manager.on_batch_end()
-
-                if (self.validate_every > 0 and self.step % self.validate_every == 0) and len(self.test_manager.callbacks):
-                    self._do_validation()
+                if self.step%self.update_every!=0:
+                    no_sync = self.ddp_model.no_sync
+                else:
+                    no_sync = contextlib.ExitStack
+                with no_sync():
+                    self.ddp_model.train()
+                    _move_dict_value_to_device(batch_x, batch_y, device=self.device, non_blocking=self.pin_memory)
+                    indices = data_iterator.get_batch_indices()
+                    # negative sampling; replace unknown; re-weight batch_y
+                    self.callback_manager.on_batch_begin(batch_x, batch_y, indices)
+                    with self.auto_cast():
+                        prediction = self._data_forward(self.ddp_model, batch_x)
+                        # edit prediction
+                        self.callback_manager.on_loss_begin(batch_y, prediction)
+                        loss = self._compute_loss(prediction, batch_y)
+
+                    avg_loss += loss.detach()
+
+                    # Is loss NaN or inf? requires_grad = False
+                    self.callback_manager.on_backward_begin(loss)
+                    self._grad_backward(loss)
+                    self.callback_manager.on_backward_end()
+                    self._update()
+                    self.callback_manager.on_step_end()
+
+                    if self.step % self.print_every == 0:
+                        avg_loss = float(avg_loss) / self.print_every
+                        print_output = "loss:{:<6.5f}".format(avg_loss)
+                        pbar.update(self.print_every)
+                        pbar.set_postfix_str(print_output)
+                        avg_loss = 0
+
+                    self.callback_manager.on_batch_end()
+
+                    if (self.validate_every > 0 and self.step % self.validate_every == 0) and len(self.test_manager.callbacks):
+                        self._do_validation()
 
             # ================= mini-batch end ==================== #
             if self.validate_every < 0 and len(self.test_manager.callbacks):

fastNLP/core/trainer.py

@@ -334,6 +334,7 @@ try:
 except:
     from .utils import _pseudo_tqdm as tqdm
 import warnings
+from pkg_resources import parse_version
 
 from .batch import DataSetIter, BatchIter
 from .callback import CallbackManager, CallbackException, Callback
@@ -473,7 +474,8 @@ class Trainer(object):
                 warnings.warn("num_workers is ignored when train_data is BatchIter.")
             if drop_last:
                 warnings.warn("drop_last is ignored when train_data is BatchIter.")
-        self.pin_memory = kwargs.get('pin_memory', True)
+        # concerning issue from https://github.com/pytorch/pytorch/issues/57273
+        self.pin_memory = kwargs.get('pin_memory', False if parse_version(torch.__version__)==parse_version('1.9') else True)
         if isinstance(model, nn.parallel.DistributedDataParallel):  # 如果是分布式的
             # device为None
             if device is not None:

fastNLP/io/data_bundle.py

@@ -31,7 +31,8 @@ class DataBundle:
         r"""
         
         :param vocabs: 从名称(字符串)到 :class:`~fastNLP.Vocabulary` 类型的dict
-        :param datasets: 从名称(字符串)到 :class:`~fastNLP.DataSet` 类型的dict
+        :param datasets: 从名称(字符串)到 :class:`~fastNLP.DataSet` 类型的dict。建议不要将相同的DataSet对象重复传入，可能会在
+            使用Pipe处理数据的时候遇到问题。
         """
         self.vocabs = vocabs or {}
         self.datasets = datasets or {}

tests/core/test_dataset.py

@@ -345,6 +345,14 @@ class TestDataSetMethods(unittest.TestCase):
         ds.apply_field(lambda x: x, 'idx', 'idx')
         self.assertTrue(isinstance(ds.get_field('idx').padder, AutoPadder))  # should be None, but AutoPadder
 
+    def test_instance_field_disappear_bug(self):
+        data = DataSet({'raw_chars': [[0,1],[2]], 'target': [0, 1]})
+        data.copy_field(field_name='raw_chars', new_field_name='chars')
+        _data = data[:1]
+        for field_name in ['raw_chars', 'target', 'chars']:
+            self.assertTrue(_data.has_field(field_name))
+
+
 class TestDataSetIter(unittest.TestCase):
     def test__repr__(self):
         ds = DataSet({"x": [[1, 2, 3, 4]] * 10, "y": [[5, 6]] * 10})