diff --git a/build_tools/VERSION.txt b/build_tools/VERSION.txt
index ccbccc3dc..a8d39b550 100644
--- a/build_tools/VERSION.txt
+++ b/build_tools/VERSION.txt
@@ -1 +1 @@
-2.2.0
+2.2.0.post0
diff --git a/tests/pytorch/test_sanity.py b/tests/pytorch/test_sanity.py
index 403164205..ece123490 100644
--- a/tests/pytorch/test_sanity.py
+++ b/tests/pytorch/test_sanity.py
@@ -46,6 +46,7 @@
     Float8CurrentScalingQuantizer,
 )
 from transformer_engine.pytorch.tensor.utils import replace_raw_data
+from transformer_engine.pytorch.distributed import checkpoint
 from test_numerics import reset_rng_states, dtype_tols
 
 # Only run FP8 tests on supported devices.
@@ -1265,3 +1266,31 @@ def test_fp8_model_init_high_precision_init_val():
     assert not hasattr(
         weight, "._high_precision_init_val"
     ), "clear_high_precision_init_val() not work"
+
+
+def test_sanity_checkpointing_on_callables():
+    """Test that TE checkpointing works correctly on callable modules."""
+
+    # torch.autograf.function
+    class MyFunction(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, inp):
+            return inp
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            return grad_output
+
+    module = MyFunction.apply
+    inp = torch.randn(10, 10, device="cuda", requires_grad=True)
+
+    out_checkpoint = checkpoint(module, inp)
+    out_checkpoint.sum().backward()
+    grad_checkpoint = inp.grad
+
+    out_standard = module(inp)
+    out_standard.sum().backward()
+    grad_standard = inp.grad
+
+    # Assert that gradients are the same
+    torch.testing.assert_close(grad_checkpoint, grad_standard)
diff --git a/transformer_engine/pytorch/distributed.py b/transformer_engine/pytorch/distributed.py
index 52ef6cbff..4304f8d3e 100644
--- a/transformer_engine/pytorch/distributed.py
+++ b/transformer_engine/pytorch/distributed.py
@@ -662,10 +662,13 @@ def checkpoint(
             **kwargs,
         )
 
-    # If this TE module is FSDP-wrapped, clear its FSDP group information because there's no need
-    # to scatter/gather activations that we will recompute anyway.
-    setattr(function, "fsdp_wrapped", False)
-    setattr(function, "fsdp_group", None)
+    from .module.base import TransformerEngineBaseModule
+
+    if isinstance(function, TransformerEngineBaseModule):
+        # If this TE module is FSDP-wrapped, clear its FSDP group information because there's no need
+        # to scatter/gather activations that we will recompute anyway.
+        setattr(function, "fsdp_wrapped", False)
+        setattr(function, "fsdp_group", None)
 
     # Otherwise discard unused te.utils.checkpoint.checkpoint() arguments
     # and execute TE's own checkpointing
diff --git a/transformer_engine/pytorch/module/base.py b/transformer_engine/pytorch/module/base.py
index 1d88355ca..669869595 100644
--- a/transformer_engine/pytorch/module/base.py
+++ b/transformer_engine/pytorch/module/base.py
@@ -1,5 +1,5 @@
 # This file was modified for portability to AMDGPU
-# Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+# Copyright (c) 2025-2026, Advanced Micro Devices, Inc. All rights reserved.
 # Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # See LICENSE for license information.
@@ -44,7 +44,7 @@
     from ..triton_kernels.cast import te_quantize_triton
 
 from ..utils import non_tn_fp8_gemm_supported
-from ..tensor.float8_tensor import Float8Quantizer 
+from ..tensor.float8_tensor import Float8CurrentScalingQuantizer, Float8Quantizer 
 
 __all__ = ["initialize_ub", "destroy_ub"]
 
@@ -1046,7 +1046,7 @@ def get_weight_workspace(
                 else:
                     current_quantizer = quantizer
                     
-            assert isinstance(current_quantizer, Float8Quantizer), "`create_tranpose_buffer=False` only availabe in `Float8Quantizer`."
+            assert isinstance(current_quantizer, Float8Quantizer) or  isinstance(current_quantizer, Float8CurrentScalingQuantizer), f"`create_tranpose_buffer=False` only availabe in `Float8Quantizer`. Not available in {current_quantizer.__class__.__name__}."
 
             # NOTE: Not create transpose buffer internally.
             current_quantizer.columnwise_usage = False