From 2f4a75e7f26fc2a6d84477545988a83409890848 Mon Sep 17 00:00:00 2001
From: Megvii Engine Team <megengine@megvii.com>
Date: Mon, 28 Sep 2020 18:44:17 +0800
Subject: [PATCH] feat(mge/utils): redesign dtype promotion

GitOrigin-RevId: 4f2fe1b6ce1430e96cb8bac34ffbbc46548007f5
---
 .../python/megengine/core/tensor/utils.py     | 101 ++++++++++++------
 1 file changed, 68 insertions(+), 33 deletions(-)

diff --git a/imperative/python/megengine/core/tensor/utils.py b/imperative/python/megengine/core/tensor/utils.py
index 9c795fbe..400e1523 100644
--- a/imperative/python/megengine/core/tensor/utils.py
+++ b/imperative/python/megengine/core/tensor/utils.py
@@ -16,39 +16,74 @@ from ..ops.special import Const
 from ..tensor.core import OpBase, TensorBase, TensorWrapperBase, apply
 
 
-def dtype_promotion(raw_inputs):
-    def add_dtype(i):
-        if type(i) == int:
-            return np.array(i, dtype=np.int32)
-        if type(i) == float:
-            return np.array(i, dtype=np.float32)
-        if type(i) == bool:
-            return np.array(i, dtype=np.bool_)
-        return None
-
-    scalar_inputs = [
-        add_dtype(i) for i in raw_inputs if not hasattr(i, "dtype") and add_dtype(i)
-    ]
-    inputs = [i for i in raw_inputs if hasattr(i, "dtype")]
-    assert len(scalar_inputs + inputs) > 0
-    dtype = None
-    if len(inputs) > 0:
-        dtype = np.result_type(*inputs)
-    dtype_all = np.result_type(*(inputs + scalar_inputs))
-    assert (
-        dtype != np.float64 and dtype != np.int64
-    ), "unsupport dtype {} by dtype_promotion, please use explict type convert".format(
-        dtype
-    )
-    if dtype_all == np.bool_:
-        for i in raw_inputs:
-            if not hasattr(i, "dtype") or i.dtype != np.bool_:
-                raise TypeError(
-                    "bool dtype can not be operated with an element without bool dtype"
-                )
-    if dtype_all == np.float64:
-        dtype_all = np.float32
-    return dtype_all
+def dtype_promotion(inputs):
+    """
+    Returns the dtype that would result from performing an arithmetic
+    operation on the provided input tensors and scalars.
+    """
+    # map numpy.dtype.kind to priority
+    category_priority = {
+        "f": 3,  # floating-point
+        "i": 2,  # signed integer
+        "u": 2,  # unsigned integer
+        "b": 1,  # boolean
+    }
+
+    def scalar2dtype(x):
+        """
+        For scalar `x`, returns its corresponding type. A floating point scalar
+        has dtype 'float32'. An integral non-boolean scalar has dtype 'int32'.
+        A boolean scalar has dtype 'bool'.
+        """
+        if isinstance(x, bool):
+            return np.bool_
+        if isinstance(x, int):
+            return np.int32
+        if isinstance(x, float):
+            return np.float32
+
+    def promote_types(types, cat):
+        """
+        Returns the data type with sufficient size to hold all types of
+        category `cat` in the list `types`.
+        """
+        used_types = [
+            i for i in types if category_priority.get(np.dtype(i).kind, 0) == cat
+        ]
+        assert len(used_types) > 0
+        res = used_types[0]
+        for i in used_types:
+            res = np.promote_types(res, i)
+        return res
+
+    def max_priority(types):
+        """
+        Returns the maximum value of the priority of each type in the list
+        `types`.
+        """
+        if not types:
+            return 0
+        else:
+            return max([category_priority.get(np.dtype(i).kind, 0) for i in types])
+
+    scalars = []
+    tensors = []
+
+    for data in inputs:
+        if hasattr(data, "dtype"):
+            tensors.append(data.dtype)
+        elif isinstance(data, (float, int, bool)):
+            scalars.append(scalar2dtype(data))
+
+    max_pri_scalars = max_priority(scalars)
+    max_pri_tensors = max_priority(tensors)
+
+    assert max_pri_scalars > 0 or max_pri_tensors > 0
+
+    if max_pri_scalars > max_pri_tensors:
+        return promote_types(scalars, max_pri_scalars)
+    else:
+        return promote_types(tensors, max_pri_tensors)
 
 
 def get_device(inputs):
-- 
GitLab