diff --git a/test/test_custom_ops.py b/test/test_custom_ops.py
index 4df2e040ceae..3f7b2562ae25 100644
--- a/test/test_custom_ops.py
+++ b/test/test_custom_ops.py
@@ -1675,6 +1675,24 @@ def foo_backward(ctx, saved, grad_out):
         (gx,) = torch.autograd.grad(y, x)
         self.assertEqual(gx, x.cos())
 
+    @parametrize(
+        "tags",
+        [
+            subtest(torch.Tag.pointwise, "single"),
+            subtest((torch.Tag.pointwise,), "tuple"),
+            subtest([torch.Tag.pointwise], "list"),
+        ],
+    )
+    def test_define_with_tags(self, tags):
+        lib = self.lib()
+        tags = (torch.Tag.pointwise,)
+        torch.library.define(
+            f"{self.test_ns}::foo", "(Tensor x) -> Tensor", lib=lib, tags=tags
+        )
+        actual = self.ns().foo.default.tags
+        self.assertTrue(isinstance(actual, list))
+        self.assertEqual(actual, list(tags))
+
     def test_define_and_impl(self):
         lib = self.lib()
         torch.library.define(f"{self.test_ns}::foo", "(Tensor x) -> Tensor", lib=lib)
diff --git a/torch/csrc/utils/python_dispatch.cpp b/torch/csrc/utils/python_dispatch.cpp
index 423386cc6536..25280c09e5fd 100644
--- a/torch/csrc/utils/python_dispatch.cpp
+++ b/torch/csrc/utils/python_dispatch.cpp
@@ -368,18 +368,20 @@ void initDispatchBindings(PyObject* module) {
           "define",
           [](const py::object& self,
              const char* schema,
-             const char* alias_analysis) {
+             const char* alias_analysis,
+             const std::vector<at::Tag>& tags) {
             auto parsed_schema =
                 torch::schema(schema, parseAliasAnalysisKind(alias_analysis));
             self.cast<torch::Library&>().def(
-                std::move(parsed_schema), {}, register_or_verify());
+                std::move(parsed_schema), tags, register_or_verify());
             // TODO: this is dumb, had to make a second copy
             return torch::schema(schema, parseAliasAnalysisKind(alias_analysis))
                 .name();
           },
           "",
           py::arg("schema"),
-          py::arg("alias_analysis") = "")
+          py::arg("alias_analysis") = "",
+          py::arg("tags") = std::vector<at::Tag>())
       .def(
           "fallback_fallthrough",
           [](py::object self, const char* dispatch) {
diff --git a/torch/library.py b/torch/library.py
index d3573056877f..1a082e9f2e90 100644
--- a/torch/library.py
+++ b/torch/library.py
@@ -71,13 +71,18 @@ def __init__(self, ns, kind, dispatch_key=""):
     def __repr__(self):
         return f"Library(kind={self.kind}, ns={self.ns}, dispatch_key={self.dispatch_key})>"
 
-    def define(self, schema, alias_analysis=""):
+    def define(self, schema, alias_analysis="", *, tags=()):
         r'''Defines a new operator and its semantics in the ns namespace.
 
         Args:
             schema: function schema to define a new operator.
             alias_analysis (optional): Indicates if the aliasing properties of the operator arguments can be
                                        inferred from the schema (default behavior) or not ("CONSERVATIVE").
+            tags (Tag | Sequence[Tag]): one or more torch.Tag to apply to this
+                                       operator. Tagging an operator changes the operator's behavior
+                                       under various PyTorch subsystems; please read the docs for the
+                                       torch.Tag carefully before applying it.
+
         Returns:
             name of the operator as inferred from the schema.
 
@@ -91,7 +96,9 @@ def define(self, schema, alias_analysis=""):
         if alias_analysis not in ["", "FROM_SCHEMA", "CONSERVATIVE"]:
             raise RuntimeError(f"Invalid alias_analysis type {alias_analysis}")
         assert self.m is not None
-        return self.m.define(schema, alias_analysis)
+        if isinstance(tags, torch.Tag):
+            tags = (tags,)
+        return self.m.define(schema, alias_analysis, tuple(tags))
 
     def impl(self, op_name, fn, dispatch_key=''):
         r'''Registers the function implementation for an operator defined in the library.
@@ -171,7 +178,7 @@ def _del_library(captured_impls, op_impls, registration_handles):
 
 
 @functools.singledispatch
-def define(qualname, schema, *, lib=None):
+def define(qualname, schema, *, lib=None, tags=()):
     r"""Defines a new operator.
 
     In PyTorch, defining an op (short for "operator") is a two step-process:
@@ -191,9 +198,13 @@ def define(qualname, schema, *, lib=None):
             avoid name collisions; a given operator may only be created once.
             If you are writing a Python library, we recommend the namespace to
             be the name of your top-level module.
-        schema (str): The schema of the operator
+        schema (str): The schema of the operator.
         lib (Optional[Library]): If provided, the lifetime of this operator
             will be tied to the lifetime of the Library object.
+        tags (Tag | Sequence[Tag]): one or more torch.Tag to apply to this
+            operator. Tagging an operator changes the operator's behavior
+            under various PyTorch subsystems; please read the docs for the
+            torch.Tag carefully before applying it.
 
     Example::
         >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LIBRARY)
@@ -222,7 +233,7 @@ def define(qualname, schema, *, lib=None):
     if lib is None:
         lib = Library(namespace, "FRAGMENT")
         _keep_alive.append(lib)
-    lib.define(name + schema, alias_analysis="")
+    lib.define(name + schema, alias_analysis="", tags=tags)
 
 
 @define.register