diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst
index 5bdd6948761..3be4d5a390e 100644
--- a/docs/source/quantization.rst
+++ b/docs/source/quantization.rst
@@ -1046,5 +1046,6 @@ Please take a look at `Limitations of Symbolic Tracing <https://docs-preview.pyt
 .. py:module:: torch.ao.sparsity
 .. py:module:: torch.ao.sparsity.experimental
 .. py:module:: torch.ao.sparsity.experimental.pruner
+.. py:module:: torch.ao.sparsity.experimental.data_sparsifier
 .. py:module:: torch.ao.sparsity.scheduler
 .. py:module:: torch.ao.sparsity.sparsifier
diff --git a/test/ao/sparsity/test_data_sparsifier.py b/test/ao/sparsity/test_data_sparsifier.py
new file mode 100644
index 00000000000..62e147c8e39
--- /dev/null
+++ b/test/ao/sparsity/test_data_sparsifier.py
@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+# Owner(s): ["module: unknown"]
+
+import logging
+
+from torch.testing._internal.common_utils import TestCase
+
+logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
+
+
+class TestBaseDataSparsifier(TestCase):
+    def test_constructor(self):
+        pass  # Nothing to test so far
diff --git a/test/test_ao_sparsity.py b/test/test_ao_sparsity.py
index 64091a4874a..d746674224c 100644
--- a/test/test_ao_sparsity.py
+++ b/test/test_ao_sparsity.py
@@ -24,5 +24,8 @@ from ao.sparsity.test_scheduler import TestScheduler  # noqa: F401
 # Composability
 from ao.sparsity.test_composability import TestComposability  # noqa: F401
 
+# Data Sparsifier
+from ao.sparsity.test_data_sparsifier import TestBaseDataSparsifier  # noqa: F401
+
 if __name__ == '__main__':
     run_tests()
diff --git a/torch/ao/sparsity/__init__.py b/torch/ao/sparsity/__init__.py
index ae635501c30..02d807eafe4 100644
--- a/torch/ao/sparsity/__init__.py
+++ b/torch/ao/sparsity/__init__.py
@@ -26,3 +26,6 @@ from .experimental.pruner.parametrization import BiasHook
 
 # Pruner
 from .experimental.pruner.base_pruner import BasePruner
+
+# Data Sparsifier
+from .experimental.data_sparsifier.base_data_sparsifier import BaseDataSparsifier
diff --git a/torch/ao/sparsity/experimental/data_sparsifier/__init__.py b/torch/ao/sparsity/experimental/data_sparsifier/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/torch/ao/sparsity/experimental/data_sparsifier/base_data_sparsifier.py b/torch/ao/sparsity/experimental/data_sparsifier/base_data_sparsifier.py
new file mode 100644
index 00000000000..aafd61cff07
--- /dev/null
+++ b/torch/ao/sparsity/experimental/data_sparsifier/base_data_sparsifier.py
@@ -0,0 +1,77 @@
+import abc
+from typing import Optional, Tuple, List, Any, Dict
+from ...sparsifier import base_sparsifier
+from collections import defaultdict
+from torch import nn
+__all__ = ['BaseDataSparsifier']
+
+
+class _Container(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+
+class BaseDataSparsifier(base_sparsifier.BaseSparsifier):
+    r"""
+    Base Data Sparsifier class for all Data sparsifiers.
+    The abstract class accepts raw torch tensors / embedding / embedding bags (refer to SUPPORTED_TYPES above)
+    to prepare for sparsification.
+    In this case, mask (and parametrizations) is owned by the class and not by the user.
+    Specifically, the container object inside the class maintains the mask and parametrizations of the input data
+
+    Args:
+        data_list (list of tuples)
+            list of (name, data) tuples to sparsify. Lookup SUPPORTED_TYPES
+            for type of data. Internally, a container module handles the data sparsification.
+
+        defaults (dict)
+            default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+    """
+    def __init__(self, data_list: Optional[List[Tuple[str, Any]]] = None, **defaults):
+        super().__init__(defaults=defaults)
+
+        self._container = _Container()
+
+        self.data_groups: Dict[str, Dict] = defaultdict(dict)  # name -> {**config}
+        if data_list is not None:
+            # add data with default config here
+            [self.add_data(name, data, **self.defaults) for name, data in data_list]
+
+    def prepare(self):
+        raise NotImplementedError("this function is undefined for this class")
+
+    def add_data(self, name: str, data, **config):
+        r""" Configures and parametrizes the internal container model with name and data
+        """
+        pass
+
+    def get_data(self, name: str):
+        r"""Returns weight tensor (or data) based on the input name.
+        """
+        pass
+
+    def __repr__(self):
+        r"""String representation of an object when printed
+        """
+        pass
+
+    def get_mask(self, name: str):
+        r"""Returns the mask currently associated with the named tensor.
+        """
+        pass
+
+    def squash_mask(self, *args, **kwargs):
+        r"""Squashes the sparse masks into the appropriate tensors.
+        """
+        pass
+
+    def step(self):
+        r"""Updates the mask for all the named data.
+        """
+        pass
+
+    @abc.abstractmethod
+    def update_mask(self, name, data, **kwargs):
+        pass