fix the bug that _DataLoaderIterMultiProcess use time to generate the seed (#43318)

* fix the bug that _DataLoaderIterMultiProcess use time to generate the seed

* use np.random.randint to generate a base seed

python/paddle/fluid/dataloader/dataloader_iter.py

@@ -374,6 +374,8 @@ class _DataLoaderIterMultiProcess(_DataLoaderIterBase):
         # see _try_put_indices
         self._thread_lock = threading.Lock()
 
+        self._base_seed = np.random.randint(low=0, high=sys.maxsize)
+
         # init workers and indices queues and put 2 indices in each indices queue
         self._init_workers()
         for _ in range(self._outstanding_capacity):
@@ -406,7 +408,8 @@ class _DataLoaderIterMultiProcess(_DataLoaderIterBase):
                       self._data_queue, self._workers_done_event,
                       self._auto_collate_batch, self._collate_fn,
                       self._drop_last, self._worker_init_fn, i,
-                      self._num_workers, self._use_shared_memory))
+                      self._num_workers, self._use_shared_memory,
+                      self._base_seed))
             worker.daemon = True
             worker.start()
             self._workers.append(worker)

python/paddle/fluid/dataloader/worker.py

@@ -257,7 +257,7 @@ def _generate_states(base_seed=0, worker_id=0):
 
 def _worker_loop(dataset, dataset_kind, indices_queue, out_queue, done_event,
                  auto_collate_batch, collate_fn, drop_last, init_fn, worker_id,
-                 num_workers, use_shared_memory):
+                 num_workers, use_shared_memory, base_seed):
     try:
         # NOTE: [ mmap files clear ] When the child process exits unexpectedly,
         # some shared memory objects may have been applied for but have not yet
@@ -272,15 +272,20 @@ def _worker_loop(dataset, dataset_kind, indices_queue, out_queue, done_event,
         try:
             import numpy as np
             import time
+            import random
         except ImportError:
             pass
         else:
-            np.random.seed(_generate_states(int(time.time()), worker_id))
+            seed = base_seed + worker_id
+            random.seed(seed)
+            paddle.seed(seed)
+            np.random.seed(_generate_states(base_seed, worker_id))
 
         global _worker_info
         _worker_info = WorkerInfo(id=worker_id,
                                   num_workers=num_workers,
-                                  dataset=dataset)
+                                  dataset=dataset,
+                                  seed=base_seed)
 
         init_exception = None
         try:

python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_exception.py

@@ -181,10 +181,11 @@ class TestDataLoaderWorkerLoop(unittest.TestCase):
                 for i in range(10):
                     indices_queue.put([i, i + 10])
                 indices_queue.put(None)
+                base_seed = 1234
                 _worker_loop(loader._dataset, 0, indices_queue,
                              loader._data_queue, loader._workers_done_event,
                              True, _collate_fn, True, _init_fn, 0, 1,
-                             loader._use_shared_memory)
+                             loader._use_shared_memory, base_seed)
                 self.assertTrue(False)
         except AssertionError:
             pass
@@ -223,10 +224,11 @@ class TestDataLoaderWorkerLoop(unittest.TestCase):
                     indices_queue.put([i, i + 10])
                 indices_queue.put(None)
                 loader._workers_done_event.set()
+                base_seed = 1234
                 _worker_loop(loader._dataset, 0, indices_queue,
                              loader._data_queue, loader._workers_done_event,
                              True, _collate_fn, True, _init_fn, 0, 1,
-                             loader._use_shared_memory)
+                             loader._use_shared_memory, base_seed)
                 self.assertTrue(True)
         except AssertionError:
             pass