DistributedBatchSampler add num_replicas and rank. test=develop (#26315)

python/paddle/incubate/hapi/distributed.py

@@ -49,6 +49,13 @@ class DistributedBatchSampler(BatchSampler):
                      `__len__` for BatchSampler to get sample
                      number of data source.
         batch_size(int): sample indice number in a mini-batch indices.
+        num_replicas(int, optional): porcess number in distributed training.
+            If :attr:`num_replicas` is None, :attr:`num_replicas` will be
+            retrieved from :code:`paddle.fluid.dygraph.parallel.ParallenEnv`.
+            Default None.
+        rank(int, optional): the rank of the current process among :attr:`num_replicas`
+            processes. If :attr:`rank` is None, :attr:`rank` is retrieved from
+            :code:`paddle.fluid.dygraph.parallel.ParallenEnv`. Default None.
         shuffle(bool): whther to shuffle indices order before genrating
             batch indices. Default False.
         drop_last(bool): whether drop the last incomplete batch dataset size
@@ -84,7 +91,13 @@ class DistributedBatchSampler(BatchSampler):
                 break
     """
 
-    def __init__(self, dataset, batch_size, shuffle=False, drop_last=False):
+    def __init__(self,
+                 dataset,
+                 batch_size,
+                 num_replicas=None,
+                 rank=None,
+                 shuffle=False,
+                 drop_last=False):
         self.dataset = dataset
 
         assert isinstance(batch_size, int) and batch_size > 0, \
@@ -96,9 +109,21 @@ class DistributedBatchSampler(BatchSampler):
         assert isinstance(drop_last, bool), \
                 "drop_last should be a boolean number"
 
-        self.drop_last = drop_last
+        if num_replicas is not None:
+            assert isinstance(num_replicas, int) and num_replicas > 0, \
+                    "num_replicas should be a positive integer"
+            self.nranks = num_replicas
+        else:
             self.nranks = ParallelEnv().nranks
+
+        if rank is not None:
+            assert isinstance(rank, int) and rank >= 0, \
+                    "rank should be a non-negative integer"
+            self.local_rank = rank
+        else:
             self.local_rank = ParallelEnv().local_rank
+
+        self.drop_last = drop_last
         self.epoch = 0
         self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.nranks))
         self.total_size = self.num_samples * self.nranks

python/paddle/incubate/hapi/tests/test_model.py

@@ -169,6 +169,12 @@ class TestModel(unittest.TestCase):
     def test_fit_static(self):
         self.fit(False)
 
+    def test_fit_dynamic_with_rank(self):
+        self.fit(True, 2, 0)
+
+    def test_fit_static_with_rank(self):
+        self.fit(False, 2, 0)
+
     def test_evaluate_dygraph(self):
         self.evaluate(True)
 
@@ -184,7 +190,7 @@ class TestModel(unittest.TestCase):
     def test_prepare_context(self):
         prepare_distributed_context()
 
-    def fit(self, dynamic):
+    def fit(self, dynamic, num_replicas=None, rank=None):
         fluid.enable_dygraph(self.device) if dynamic else None
         seed = 333
         fluid.default_startup_program().random_seed = seed
@@ -204,9 +210,17 @@ class TestModel(unittest.TestCase):
         np.testing.assert_allclose(result['acc'], self.acc1)
 
         train_sampler = DistributedBatchSampler(
-            self.train_dataset, batch_size=64, shuffle=False)
+            self.train_dataset,
+            batch_size=64,
+            shuffle=False,
+            num_replicas=num_replicas,
+            rank=rank)
         val_sampler = DistributedBatchSampler(
-            self.val_dataset, batch_size=64, shuffle=False)
+            self.val_dataset,
+            batch_size=64,
+            shuffle=False,
+            num_replicas=num_replicas,
+            rank=rank)
 
         train_loader = fluid.io.DataLoader(
             self.train_dataset,