diff --git a/torchrec/distributed/batched_embedding_kernel.py b/torchrec/distributed/batched_embedding_kernel.py
index e0a19547c..e22c829b0 100644
--- a/torchrec/distributed/batched_embedding_kernel.py
+++ b/torchrec/distributed/batched_embedding_kernel.py
@@ -776,7 +776,11 @@ def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
         if weights is not None and not torch.is_floating_point(weights):
             weights = None
         if features.variable_stride_per_key() and isinstance(
-            self.emb_module, SplitTableBatchedEmbeddingBagsCodegen
+            self.emb_module,
+            (
+                SplitTableBatchedEmbeddingBagsCodegen,
+                DenseTableBatchedEmbeddingBagsCodegen,
+            ),
         ):
             return self.emb_module(
                 indices=features.values().long(),
diff --git a/torchrec/distributed/sharding/dp_sharding.py b/torchrec/distributed/sharding/dp_sharding.py
index 020beb012..6ffb52e4c 100644
--- a/torchrec/distributed/sharding/dp_sharding.py
+++ b/torchrec/distributed/sharding/dp_sharding.py
@@ -153,10 +153,6 @@ def forward(
             Awaitable[Awaitable[SparseFeatures]]: awaitable of awaitable of SparseFeatures.
         """
 
-        if sparse_features.variable_stride_per_key():
-            raise ValueError(
-                "Dense TBE kernel does not support variable batch per feature"
-            )
         return NoWait(cast(Awaitable[KeyedJaggedTensor], NoWait(sparse_features)))
 
 
diff --git a/torchrec/distributed/test_utils/test_model_parallel.py b/torchrec/distributed/test_utils/test_model_parallel.py
index de4923207..c6d81b8c2 100644
--- a/torchrec/distributed/test_utils/test_model_parallel.py
+++ b/torchrec/distributed/test_utils/test_model_parallel.py
@@ -245,15 +245,11 @@ def test_sharding_rw(
                 SharderType.EMBEDDING_BAG_COLLECTION.value,
             ]
         ),
-        kernel_type=st.sampled_from(
-            [
-                EmbeddingComputeKernel.DENSE.value,
-            ],
-        ),
-        apply_optimizer_in_backward_config=st.sampled_from([None]),
+        kernel_type=st.just(EmbeddingComputeKernel.DENSE.value),
+        apply_optimizer_in_backward_config=st.just(None),
         # TODO - need to enable optimizer overlapped behavior for data_parallel tables
     )
-    @settings(verbosity=Verbosity.verbose, max_examples=3, deadline=None)
+    @settings(verbosity=Verbosity.verbose, max_examples=1, deadline=None)
     def test_sharding_dp(
         self,
         sharder_type: str,
@@ -591,12 +587,13 @@ def test_sharding_twrw(
                 ShardingType.TABLE_WISE.value,
                 ShardingType.COLUMN_WISE.value,
                 ShardingType.ROW_WISE.value,
+                ShardingType.DATA_PARALLEL.value,
             ]
         ),
         global_constant_batch=st.booleans(),
         pooling=st.sampled_from([PoolingType.SUM, PoolingType.MEAN]),
     )
-    @settings(verbosity=Verbosity.verbose, max_examples=6, deadline=None)
+    @settings(verbosity=Verbosity.verbose, max_examples=10, deadline=None)
     def test_sharding_variable_batch(
         self,
         sharding_type: str,
@@ -608,13 +605,18 @@ def test_sharding_variable_batch(
             self.skipTest(
                 "bounds_check_indices on CPU does not support variable length (batch size)"
             )
+        kernel = (
+            EmbeddingComputeKernel.DENSE.value
+            if sharding_type == ShardingType.DATA_PARALLEL.value
+            else EmbeddingComputeKernel.FUSED.value
+        )
         self._test_sharding(
             # pyre-ignore[6]
             sharders=[
                 create_test_sharder(
                     sharder_type=SharderType.EMBEDDING_BAG_COLLECTION.value,
                     sharding_type=sharding_type,
-                    kernel_type=EmbeddingComputeKernel.FUSED.value,
+                    kernel_type=kernel,
                     device=self.device,
                 ),
             ],