diff --git a/test/prototype/mx_formats/test_kernels.py b/test/prototype/mx_formats/test_kernels.py
index 240e8eea49..14de3610b3 100644
--- a/test/prototype/mx_formats/test_kernels.py
+++ b/test/prototype/mx_formats/test_kernels.py
@@ -521,8 +521,9 @@ def test_triton_mxfp8_dim0_zeros():
 )
 @pytest.mark.parametrize("M", (256, 2048, 131072))
 @pytest.mark.parametrize("K", (256, 5120, 7168))
-def test_triton_mxfp8_dequant_dim0(M, K):
-    x = torch.zeros(M, K, dtype=torch.bfloat16, device="cuda")
+@pytest.mark.parametrize("orig_dtype", (torch.float32, torch.bfloat16))
+def test_triton_mxfp8_dequant_dim0(M, K, orig_dtype):
+    x = torch.zeros(M, K, dtype=orig_dtype, device="cuda")
     block_size = 32
     x_data, x_scales = triton_to_mxfp8_dim0_reference(x, block_size=32)
     hp_ref = to_dtype(
@@ -530,9 +531,9 @@ def test_triton_mxfp8_dequant_dim0(M, K):
         x_scales,
         torch.float8_e4m3fn,
         block_size,
-        torch.bfloat16,
+        orig_dtype,
     )
-    hp_t = triton_mxfp8_dequant_dim0(x_data, x_scales, torch.bfloat16, block_size)
+    hp_t = triton_mxfp8_dequant_dim0(x_data, x_scales, orig_dtype, block_size)
     torch.testing.assert_close(hp_t, hp_ref, rtol=0, atol=0)
 
 
diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py
index c085ed9740..671186c7c7 100644
--- a/torchao/prototype/mx_formats/kernels.py
+++ b/torchao/prototype/mx_formats/kernels.py
@@ -1141,7 +1141,8 @@ def triton_to_mxfp8_dim0(
         * `scale`: the `e8m0` values of `x_scale` used to cast `x` to mxfp8 across dim0
         """
         assert x.is_contiguous(), "`x` must be contiguous"
-        assert inner_block_size <= 32
+        assert inner_block_size <= 32, "inner_block_size must be <= 32"
+        assert x.dtype == torch.bfloat16, "only bfloat16 inputs are supported"
 
         # Reshape tensor to 2d if necessary and get shape
         x_orig_shape = x.shape
@@ -1279,12 +1280,13 @@ def triton_to_mxfp8_dim1_reference(
             scale_e8m0_dim1,
         )
 
+    @triton_op("torchao::triton_mxfp8_dequant_dim0", mutates_args={})
     def triton_mxfp8_dequant_dim0(
         e4m3_data: torch.Tensor,
         e8m0_scales: torch.Tensor,
         out_dtype: torch.dtype,
         scale_block_size: int = 32,
-    ) -> None:
+    ) -> torch.Tensor:
         assert scale_block_size == 32, "scale_block_size must be 32 for now"
         assert out_dtype in (torch.bfloat16, torch.float32), (
             "out_dtype must be bf16 or fp32"
@@ -1300,7 +1302,7 @@ def triton_mxfp8_dequant_dim0(
             triton.cdiv(e4m3_data.shape[0], META["ROW_TILE_SIZE"]),
             triton.cdiv(e4m3_data.shape[1], META["COL_TILE_SIZE"]),
         )
-        _dequant_mxfp8_kernel[grid](
+        wrap_triton(_dequant_mxfp8_kernel)[grid](
             e4m3_data,
             e8m0_scales.to(torch.uint8),
             out_buffer,
@@ -1371,8 +1373,8 @@ def _dequant_mxfp8_kernel(
 
     @triton.jit
     def _e8m0_to_fp32(scale_e8m0):
-        e8m0_exponent_bias = 127
         e8m0_nan_val = 255
+        e8m0_exponent_bias = 127
         s_offset = scale_e8m0.to(tl.int16) - e8m0_exponent_bias
         s_fp = tl.exp2(s_offset.to(tl.float32))
         s_fp = tl.where(scale_e8m0 != e8m0_nan_val, s_fp, float("nan"))