diff --git a/monai/data/samplers.py b/monai/data/samplers.py
index 5fea6959de..8bba79c9b0 100644
--- a/monai/data/samplers.py
+++ b/monai/data/samplers.py
@@ -70,9 +70,6 @@ class DistributedWeightedRandomSampler(DistributedSampler):
         num_samples_per_rank: number of samples to draw for every rank, sample from
             the distributed subset of dataset.
             if None, default to the length of dataset split by DistributedSampler.
-        replacement: if ``True``, samples are drawn with replacement, otherwise, they are
-            drawn without replacement, which means that when a sample index is drawn for a row,
-            it cannot be drawn again for that row, default to True.
         generator: PyTorch Generator used in sampling.
         even_divisible: if False, different ranks can have different data length.
             for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].'
@@ -90,7 +87,6 @@ def __init__(
         dataset: Dataset,
         weights: Sequence[float],
         num_samples_per_rank: Optional[int] = None,
-        replacement: bool = True,
         generator: Optional[torch.Generator] = None,
         even_divisible: bool = True,
         num_replicas: Optional[int] = None,
@@ -107,16 +103,17 @@ def __init__(
             **kwargs,
         )
         self.weights = weights
-        self.num_samples_per_rank = num_samples_per_rank
-        self.replacement = replacement
+        self.num_samples_per_rank = num_samples_per_rank if num_samples_per_rank is not None else self.num_samples
         self.generator = generator
 
     def __iter__(self):
         indices = list(super().__iter__())
-        num_samples = self.num_samples_per_rank if self.num_samples_per_rank is not None else self.num_samples
         weights = torch.as_tensor([self.weights[i] for i in indices], dtype=torch.double)
         # sample based on the provided weights
-        rand_tensor = torch.multinomial(weights, num_samples, self.replacement, generator=self.generator)
+        rand_tensor = torch.multinomial(weights, self.num_samples_per_rank, True, generator=self.generator)
 
         for i in rand_tensor:
             yield indices[i]
+
+    def __len__(self):
+        return self.num_samples_per_rank
diff --git a/tests/test_distributed_weighted_random_sampler.py b/tests/test_distributed_weighted_random_sampler.py
index 6e27e78d4c..b8e088fdcf 100644
--- a/tests/test_distributed_weighted_random_sampler.py
+++ b/tests/test_distributed_weighted_random_sampler.py
@@ -21,12 +21,11 @@
 
 class DistributedWeightedRandomSamplerTest(DistTestCase):
     @DistCall(nnodes=1, nproc_per_node=2)
-    def test_replacement(self):
+    def test_sampling(self):
         data = [1, 2, 3, 4, 5]
         weights = [1, 2, 3, 4, 5]
         sampler = DistributedWeightedRandomSampler(
             weights=weights,
-            replacement=True,
             dataset=data,
             shuffle=False,
             generator=torch.Generator().manual_seed(0),
@@ -46,7 +45,6 @@ def test_num_samples(self):
         sampler = DistributedWeightedRandomSampler(
             weights=weights,
             num_samples_per_rank=5,
-            replacement=True,
             dataset=data,
             shuffle=False,
             generator=torch.Generator().manual_seed(123),