fix a couple of issues

Author: xadupre

_unittests/ut_helpers/test_helper.py

@@ -333,6 +333,7 @@ def test_size_type_onnx(self):
                     "FLOAT8E5M2",
                     "FLOAT8E4M3FN",
                     "FLOAT8E4M3FNUZ",
+                    "FLOAT8E8M0",
                 }:
                     onnx_dtype_to_torch_dtype(i)
                     onnx_dtype_to_np_dtype(i)

onnx_diagnostic/helpers/helper.py

@@ -36,11 +36,12 @@ def size_type(dtype: Any) -> int:
             TensorProto.FLOAT8E4M3FNUZ,
             TensorProto.FLOAT8E5M2,
             TensorProto.FLOAT8E5M2FNUZ,
+            getattr(TensorProto, "FLOAT8E8M0", None),
         }:
             return 1
         if dtype in {TensorProto.COMPLEX128}:
             return 16
-        from .helpers.onnx_helper import onnx_dtype_name
+        from .onnx_helper import onnx_dtype_name
 
         raise AssertionError(
             f"Unable to return the element size for type {onnx_dtype_name(dtype)}"

onnx_diagnostic/reference/ops/op_cast_like.py

@@ -11,22 +11,26 @@
         float8e5m2fnuz,
     )
 except ImportError:
+    bfloat16 = None
     from onnx.reference.ops.op_cast import cast_to
 from ...helpers.onnx_helper import np_dtype_to_tensor_dtype
 
 
 def _cast_like(x, y, saturate):
-    if y.dtype == bfloat16 and y.dtype.descr[0][0] == "bfloat16":
-        # np.uint16 == np.uint16 is True as well as np.uint16 == bfloat16
-        to = TensorProto.BFLOAT16
-    elif y.dtype == float8e4m3fn and y.dtype.descr[0][0] == "e4m3fn":
-        to = TensorProto.FLOAT8E4M3FN
-    elif y.dtype == float8e4m3fnuz and y.dtype.descr[0][0] == "e4m3fnuz":
-        to = TensorProto.FLOAT8E4M3FNUZ
-    elif y.dtype == float8e5m2 and y.dtype.descr[0][0] == "e5m2":
-        to = TensorProto.FLOAT8E5M2
-    elif y.dtype == float8e5m2fnuz and y.dtype.descr[0][0] == "e5m2fnuz":
-        to = TensorProto.FLOAT8E5M2FNUZ
+    if bfloat16 is not None:
+        if y.dtype == bfloat16 and y.dtype.descr[0][0] == "bfloat16":
+            # np.uint16 == np.uint16 is True as well as np.uint16 == bfloat16
+            to = TensorProto.BFLOAT16
+        elif y.dtype == float8e4m3fn and y.dtype.descr[0][0] == "e4m3fn":
+            to = TensorProto.FLOAT8E4M3FN
+        elif y.dtype == float8e4m3fnuz and y.dtype.descr[0][0] == "e4m3fnuz":
+            to = TensorProto.FLOAT8E4M3FNUZ
+        elif y.dtype == float8e5m2 and y.dtype.descr[0][0] == "e5m2":
+            to = TensorProto.FLOAT8E5M2
+        elif y.dtype == float8e5m2fnuz and y.dtype.descr[0][0] == "e5m2fnuz":
+            to = TensorProto.FLOAT8E5M2FNUZ
+        else:
+            to = np_dtype_to_tensor_dtype(y.dtype)  # type: ignore
     else:
         to = np_dtype_to_tensor_dtype(y.dtype)  # type: ignore
     return (cast_to(x, to, saturate),)