减少batch中不断创建多进程的开销

fastNLP/core/batch.py

@@ -15,24 +15,28 @@ from fastNLP.core.sampler import RandomSampler
 
 class Batch(object):
     def __init__(self, dataset, batch_size, sampler=RandomSampler(), as_numpy=False, num_workers=0, pin_memory=False,
-                 timeout=0.0):
+                 timeout=0.0, keep_process=False):
         """
         Batch is an iterable object which iterates over mini-batches.
 
             Example::
-
-                for batch_x, batch_y in Batch(data_set, batch_size=16, sampler=SequentialSampler()):
-                    # ...
+                iterator = Batch(data_set, batch_size=16, sampler=SequentialSampler())
+                for epoch in range(num_epochs):
+                    for batch_x, batch_y in iterator: # 每次epoch会重新使用sampler生成index的。
+                        # ...
 
         :param DataSet dataset: a DataSet object
         :param int batch_size: the size of the batch
         :param Sampler sampler: a Sampler object
-        :param bool as_numpy: If True, return Numpy array when possible. Otherwise, return torch tensors.
+        :param bool as_numpy: If True, return Numpy array. Otherwise, return torch tensors.
         :param num_workers: int, 使用多少个进程来准备数据。默认为0, 即使用主线程生成数据。 特性处于实验阶段，谨慎使用。
             如果DataSet较大，且每个batch的准备时间很短，使用多进程可能并不能提速。
         :param pin_memory: bool, 默认为False. 设置为True时，有可能可以节省tensor从cpu移动到gpu的阻塞时间。
         :param timeout: float, 大于0的数，只有在num_workers>0时才有用。超过该时间仍然没有获取到一个batch则报错，可以用于
             检测是否出现了batch产生阻塞的情况。
+        :param keep_process: bool. 默认为False，该参数只在多进程下有效。在多进程的情况下，反复产生batch的iterator会导致
+            不断创建、销毁进程，可能对速度有一定的影响。当keep_process为True时，直到Batch对象被删除之前，多进程都没有关
+            闭。如果设置了keep_process为True，可以通过del BatchObject来删除Batch对象并关闭进程。
         """
 
         if num_workers < 0:
@@ -45,15 +49,24 @@ class Batch(object):
         self.batch_size = batch_size
         self.sampler = sampler
         self.num_workers = num_workers
+        self.keep_process = keep_process
         self.pin_memory = pin_memory
         self.timeout = timeout
         self.as_numpy = as_numpy
         self.num_batches = len(dataset) // batch_size + int(len(dataset) % batch_size != 0)
         self.cur_batch_indices = None
+        self._data_iterator = None
 
     def __iter__(self):
-        # TODO 现在多线程的情况下每个循环都会重新创建多进程，开销可能有点大。可以考虑直接复用iterator.
-        return _DataLoaderIter(self)
+        if self._data_iterator is not None:
+            # 重新设置index_list
+            self._data_iterator.reset()
+            return self._data_iterator
+        elif self.keep_process and self.num_workers>0:
+            self._data_iterator = _DataLoaderIter(self)
+            return self._data_iterator
+        else: # 大多数情况是这个
+            return _DataLoaderIter(self)
 
     def __len__(self):
         return self.num_batches
@@ -61,6 +74,12 @@ class Batch(object):
     def get_batch_indices(self):
         return self.cur_batch_indices
 
+    def __del__(self):
+        if self.keep_process is True:
+            del self._data_iterator
+
+
+
 def to_tensor(batch, dtype):
     try:
         if dtype in (int, np.int8, np.int16, np.int32, np.int64):
@@ -276,6 +295,7 @@ class _DataLoaderIter(object):
         self.num_workers = batcher.num_workers
         self.pin_memory = batcher.pin_memory and torch.cuda.is_available()
         self.timeout = batcher.timeout
+        self.keep_process = batcher.keep_process
         self.done_event = threading.Event()
         self.curidx = 0
         self.idx_list = self.sampler(self.dataset)
@@ -335,6 +355,17 @@ class _DataLoaderIter(object):
             for _ in range(2 * self.num_workers):
                 self._put_indices()
 
+    def reset(self):
+        """
+        重置curidx以及重新采样idx_list. 只有再需要keep_process时才有用
+        :return:
+        """
+        if self.keep_process:
+            self.curidx = 0
+            self.idx_list = self.sampler(self.dataset)
+            for _ in range(2 * self.num_workers):
+                self._put_indices()
+
     def _get_batch(self):
         if self.timeout > 0:
             try:
@@ -366,7 +397,8 @@ class _DataLoaderIter(object):
 
         # 如果生成的数据为0了，则停止
         if self.batches_outstanding == 0:
-            self._shutdown_workers()
+            if not self.keep_process:
+                self._shutdown_workers()
             raise StopIteration
 
         while True:
@@ -449,4 +481,4 @@ class _DataLoaderIter(object):
 
     def __del__(self):
         if self.num_workers > 0:
-            self._shutdown_workers()
+            self._shutdown_workers()

fastNLP/core/trainer.py

@@ -61,7 +61,8 @@ class Trainer(object):
         :param BaseSampler sampler: method used to generate batch data.
         :param num_workers: int, 使用多少个进程来准备数据。默认为0, 即使用主线程生成数据。 特性处于实验阶段，谨慎使用。
             如果DataSet较大，且每个batch的准备时间很短，使用多进程可能并不能提速。
-        :param pin_memory: bool, 默认为False. 设置为True时，有可能可以节省tensor从cpu移动到gpu的阻塞时间。
+        :param pin_memory: bool, 默认为False. 当设置为True时，会使用锁页内存，可能导致内存占用变多。如果内存比较充足，
+            可以考虑设置为True进行加速, 当pin_memory为True时，默认使用non_blocking=True的方式将数据从cpu移动到gpu。
         :param timeout: float, 大于0的数，只有在num_workers>0时才有用。超过该时间仍然没有获取到一个batch则报错，可以用于
             检测是否出现了batch产生阻塞的情况。
         :param bool use_tqdm: whether to use tqdm to show train progress.
@@ -246,7 +247,8 @@ class Trainer(object):
         with inner_tqdm(total=total_steps, postfix='loss:{0:<6.5f}', leave=False, dynamic_ncols=True) as pbar:
             avg_loss = 0
             data_iterator = Batch(self.train_data, batch_size=self.batch_size, sampler=self.sampler, as_numpy=False,
-                                  num_workers=self.num_workers, pin_memory=self.pin_memory, timeout=self.timeout)
+                                  num_workers=self.num_workers, pin_memory=self.pin_memory, timeout=self.timeout,
+                                  keep_process=True)
             for epoch in range(1, self.n_epochs+1):
                 pbar.set_description_str(desc="Epoch {}/{}".format(epoch, self.n_epochs))
                 # early stopping
@@ -255,7 +257,8 @@ class Trainer(object):
                     indices = data_iterator.get_batch_indices()
                     # negative sampling; replace unknown; re-weight batch_y
                     self.callback_manager.before_batch(batch_x, batch_y, indices)
-                    _move_dict_value_to_device(batch_x, batch_y, device=self._model_device)
+                    _move_dict_value_to_device(batch_x, batch_y, device=self._model_device,
+                                               non_blocking=self.pin_memory) # pin_memory, use non_blockling.
                     prediction = self._data_forward(self.model, batch_x)
 
                     # edit prediction

test/core/test_trainer.py

@@ -237,6 +237,31 @@ class TrainerTestGround(unittest.TestCase):
                 use_tqdm=False,
                 print_every=2)
 
-    def test_case2(self):
-        # check metrics Wrong
-        data_set = prepare_fake_dataset2('x1', 'x2')
+    def test_trainer_multiprocess(self):
+        dataset = prepare_fake_dataset2('x1', 'x2')
+        dataset.set_input('x1', 'x2', 'y', flag=True)
+
+        class Model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc = nn.Linear(5, 4)
+
+            def forward(self, x1, x2, y):
+                x1 = self.fc(x1)
+                x2 = self.fc(x2)
+                x = x1 + x2
+                loss = F.cross_entropy(x, y)
+                return {'loss': loss}
+
+        model = Model()
+        trainer = Trainer(
+            train_data=dataset,
+            model=model,
+            use_tqdm=True,
+            print_every=2,
+            num_workers=2,
+            pin_memory=False,
+            timeout=0,
+        )
+        trainer.train()
+