diff --git a/docs/source/amp.rst b/docs/source/amp.rst
index d0ef865b370..a9d98a0aa09 100644
--- a/docs/source/amp.rst
+++ b/docs/source/amp.rst
@@ -40,6 +40,10 @@ For CUDA and CPU, APIs are also provided separately:
 
 Autocasting
 ^^^^^^^^^^^
+.. currentmodule:: torch.amp.autocast_mode
+
+.. autofunction::  is_autocast_available
+
 .. currentmodule:: torch
 
 .. autoclass:: autocast
diff --git a/test/test_autocast.py b/test/test_autocast.py
index 50549449328..5b1e38eff06 100644
--- a/test/test_autocast.py
+++ b/test/test_autocast.py
@@ -340,6 +340,8 @@ class TestTorchAutocast(TestCase):
         with self.assertRaisesRegex(RuntimeError, msg):
             with torch.autocast(device_type=dev):
                 _ = torch.tensor(1)
+        with self.assertRaisesRegex(RuntimeError, msg):
+            assert torch.amp.is_autocast_available(device_type=dev)
 
 
 if __name__ == "__main__":
diff --git a/torch/amp/__init__.py b/torch/amp/__init__.py
index e0be6969755..2884dfeefe3 100644
--- a/torch/amp/__init__.py
+++ b/torch/amp/__init__.py
@@ -1,2 +1,7 @@
-from .autocast_mode import _enter_autocast, _exit_autocast, autocast
+from .autocast_mode import (
+    _enter_autocast,
+    _exit_autocast,
+    autocast,
+    is_autocast_available,
+)
 from .grad_scaler import GradScaler
diff --git a/torch/amp/autocast_mode.py b/torch/amp/autocast_mode.py
index 87ff709fcfb..523d8dc34d5 100644
--- a/torch/amp/autocast_mode.py
+++ b/torch/amp/autocast_mode.py
@@ -6,7 +6,19 @@ from typing import Any, Optional
 import torch
 from torch.types import _dtype
 
-__all__ = ["autocast_decorator", "autocast"]
+__all__ = ["autocast_decorator", "autocast", "is_autocast_available"]
+
+
+def is_autocast_available(device_type: str) -> bool:
+    r"""
+    Return a bool indicating if autocast is available on :attr:`device_type`.
+
+    Args:
+        device_type(str):  Device type to use. Possible values are: 'cuda', 'cpu', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+    return torch._C._is_autocast_available(device_type)
 
 
 def autocast_decorator(autocast_instance, func):
@@ -199,7 +211,7 @@ class autocast:
             assert dtype is not None
             return
         self.device = device_type
-        if not torch._C._is_autocast_available(self.device):
+        if not is_autocast_available(self.device):
             raise RuntimeError(
                 f"User specified an unsupported autocast device_type '{self.device}'"
             )
diff --git a/torch/utils/checkpoint.py b/torch/utils/checkpoint.py
index ca0e39d5379..7c74b4c6be7 100644
--- a/torch/utils/checkpoint.py
+++ b/torch/utils/checkpoint.py
@@ -194,7 +194,7 @@ def set_device_states(devices, states) -> None:
 
 
 def _get_autocast_kwargs(device="cuda"):
-    if torch._C._is_autocast_available(device):
+    if torch.amp.is_autocast_available(device):
         device_autocast_kwargs = {
             "enabled": torch.is_autocast_enabled(device),
             "dtype": torch.get_autocast_dtype(device),
@@ -289,7 +289,7 @@ class CheckpointFunction(torch.autograd.Function):
 
             device_autocast_ctx = device_module.amp.autocast(
                 **ctx.device_autocast_kwargs
-            ) if torch._C._is_autocast_available(ctx.device) else contextlib.nullcontext()
+            ) if torch.amp.is_autocast_available(ctx.device) else contextlib.nullcontext()
             with torch.enable_grad(), device_autocast_ctx, \
                  torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):
                 outputs = ctx.run_function(*detached_inputs)
@@ -1396,7 +1396,7 @@ def _checkpoint_without_reentrant_generator(
 
             device_autocast_ctx = device_module.amp.autocast(
                 **device_autocast_kwargs
-            ) if torch._C._is_autocast_available(device) else contextlib.nullcontext()
+            ) if torch.amp.is_autocast_available(device) else contextlib.nullcontext()
             with device_autocast_ctx, torch.cpu.amp.autocast(**cpu_autocast_kwargs), \
                  recompute_context:
                 fn(*args, **kwargs)