refactor: simplify dataset construction

deepmd/pt/utils/dataloader.py

@@ -1,11 +1,16 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import logging
 import os
-import queue
 import time
-from multiprocessing.dummy import (
+from functools import (
+    partial,
+)
+from multiprocessing import (
     Pool,
 )
+from queue import (
+    Queue,
+)
 from threading import (
     Thread,
 )
@@ -52,6 +57,13 @@ def setup_seed(seed) -> None:
     torch.backends.cudnn.deterministic = True
 
 
+def construct_dataset(system, type_map):
+    return DeepmdDataSetForLoader(
+        system=system,
+        type_map=type_map,
+    )
+
+
 class DpLoaderSet(Dataset):
     """A dataset for storing DataLoaders to multiple Systems.
 
@@ -87,11 +99,7 @@ def __init__(
         if len(systems) >= 100:
             log.info(f"Constructing DataLoaders from {len(systems)} systems")
 
-        def construct_dataset(system):
-            return DeepmdDataSetForLoader(
-                system=system,
-                type_map=type_map,
-            )
+        construct_dataset_systems = partial(construct_dataset, type_map=type_map)
 
         with Pool(
             os.cpu_count()
@@ -101,7 +109,7 @@ def construct_dataset(system):
                 else 1
             )
         ) as pool:
-            self.systems = pool.map(construct_dataset, systems)
+            self.systems = pool.map(construct_dataset_systems, systems)
 
         self.sampler_list: list[DistributedSampler] = []
         self.index = []
@@ -185,85 +193,65 @@ def print_summary(
         name: str,
         prob: list[float],
     ) -> None:
-        print_summary(
-            name,
-            len(self.systems),
-            [ss.system for ss in self.systems],
-            [ss._natoms for ss in self.systems],
-            self.batch_sizes,
-            [
-                ss._data_system.get_sys_numb_batch(self.batch_sizes[ii])
-                for ii, ss in enumerate(self.systems)
-            ],
-            prob,
-            [ss._data_system.pbc for ss in self.systems],
-        )
-
-
-_sentinel = object()
-QUEUESIZE = 32
+        rank = dist.get_rank() if dist.is_initialized() else 0
+        if rank == 0:
+            print_summary(
+                name,
+                len(self.systems),
+                [ss.system for ss in self.systems],
+                [ss._natoms for ss in self.systems],
+                self.batch_sizes,
+                [
+                    ss._data_system.get_sys_numb_batch(self.batch_sizes[ii])
+                    for ii, ss in enumerate(self.systems)
+                ],
+                prob,
+                [ss._data_system.pbc for ss in self.systems],
+            )
 
 
 class BackgroundConsumer(Thread):
-    def __init__(self, queue, source, max_len) -> None:
-        Thread.__init__(self)
+    def __init__(self, queue, source) -> None:
+        super().__init__()
+        self.daemon = True
         self._queue = queue
         self._source = source  # Main DL iterator
-        self._max_len = max_len  #
 
     def run(self) -> None:
         for item in self._source:
             self._queue.put(item)  # Blocking if the queue is full
 
-        # Signal the consumer we are done.
-        self._queue.put(_sentinel)
+        # Signal the consumer we are done; this should not happen for DataLoader
+        self._queue.put(StopIteration)
+
+
+QUEUESIZE = 32
 
 
 class BufferedIterator:
     def __init__(self, iterable) -> None:
-        self._queue = queue.Queue(QUEUESIZE)
+        self._queue = Queue(QUEUESIZE)
         self._iterable = iterable
-        self._consumer = None
-
-        self.start_time = time.time()
-        self.warning_time = None
-        self.total = len(iterable)
-
-    def _create_consumer(self) -> None:
-        self._consumer = BackgroundConsumer(self._queue, self._iterable, self.total)
-        self._consumer.daemon = True
+        self._consumer = BackgroundConsumer(self._queue, self._iterable)
         self._consumer.start()
+        self.len = len(iterable)
 
     def __iter__(self):
         return self
 
     def __len__(self) -> int:
-        return self.total
+        return self.len
 
     def __next__(self):
-        # Create consumer if not created yet
-        if self._consumer is None:
-            self._create_consumer()
-        # Notify the user if there is a data loading bottleneck
-        if self._queue.qsize() < min(2, max(1, self._queue.maxsize // 2)):
-            if time.time() - self.start_time > 5 * 60:
-                if (
-                    self.warning_time is None
-                    or time.time() - self.warning_time > 15 * 60
-                ):
-                    log.warning(
-                        "Data loading buffer is empty or nearly empty. This may "
-                        "indicate a data loading bottleneck, and increasing the "
-                        "number of workers (--num-workers) may help."
-                    )
-                    self.warning_time = time.time()
-
-        # Get next example
+        start_wait = time.time()
         item = self._queue.get()
+        wait_time = time.time() - start_wait
+        if (
+            wait_time > 1.0
+        ):  # Even for Multi-Task training, each step usually takes < 1s
+            log.warning(f"Data loading is slow, waited {wait_time:.2f} seconds.")
         if isinstance(item, Exception):
             raise item
-        if item is _sentinel:
-            raise StopIteration
         return item
 
 

deepmd/utils/path.py

@@ -44,6 +44,9 @@ def __new__(cls, path: str, mode: str = "r"):
             raise FileNotFoundError(f"{path} not found")
         return super().__new__(cls)
 
+    def __getnewargs__(self):
+        return (self.path, self.mode)
+
     @abstractmethod
     def load_numpy(self) -> np.ndarray:
         """Load NumPy array.