fix(mge/functional): convert input type to float32 for more elemwise op

GitOrigin-RevId: cf3bf8cb80

imperative/python/megengine/core/tensor/array_method.py

@@ -27,9 +27,31 @@ from .utils import setscalar
 _ElwMod = Elemwise.Mode
 
 
-def _elwise(*args, mode):
+def _elwise_apply(args, mode):
     op = builtin.Elemwise(mode)
-    if mode in (_ElwMod.TRUE_DIV, _ElwMod.POW):
+    _isscalar = True
+    for i in args:
+        if isscalar(i) == False:
+            _isscalar = False
+            break
+    (result,) = apply(op, *args)
+    if _isscalar:
+        setscalar(result)
+    return result
+
+
+def _elwise(*args, mode):
+    if mode in (
+        _ElwMod.TRUE_DIV,
+        _ElwMod.POW,
+        _ElwMod.CEIL,
+        _ElwMod.FLOOR,
+        _ElwMod.ROUND,
+    ):
+        if mode in (_ElwMod.CEIL, _ElwMod.FLOOR, _ElwMod.ROUND) and np.issubdtype(
+            args[0].dtype, np.integer
+        ):
+            return args[0]
         args = tuple(
             map(
                 lambda x: x.astype("float32")
@@ -39,16 +61,7 @@ def _elwise(*args, mode):
             )
         )
     args = utils.convert_inputs(*args)
-    (result,) = apply(op, *args)
-
-    _isscalar = True
-    for i in args:
-        if isscalar(i) == False:
-            _isscalar = False
-            break
-    if _isscalar:
-        setscalar(result)
-    return result
+    return _elwise_apply(args, mode)
 
 
 def _matmul(inp1, inp2):

imperative/python/megengine/functional/elemwise.py

@@ -9,10 +9,13 @@
 # pylint: disable=unused-argument,invalid-name,redefined-builtin,arguments-out-of-order
 import functools
 
+import numpy as np
+
 from ..core._imperative_rt.core2 import apply
 from ..core.ops import builtin
 from ..core.ops.builtin import Elemwise
 from ..core.tensor import megbrain_graph, utils
+from ..core.tensor.array_method import _elwise_apply
 from ..core.tensor.utils import isscalar, setscalar
 from ..device import get_default_device
 from ..jit.tracing import is_tracing
@@ -74,7 +77,6 @@ __all__ = [
 
 
 def _elwise(*args, mode):
-    op = builtin.Elemwise(mode)
     tensor_args = list(
         filter(lambda x: isinstance(x, (Tensor, megbrain_graph.VarNode)), args)
     )
@@ -84,17 +86,33 @@ def _elwise(*args, mode):
         args = utils.convert_inputs(first_arg, *args[1:])
     else:
         args = utils.convert_inputs(*args)
-    if mode in ("true_div", "exp", "pow", "log", "expm1", "log1p"):
+    if mode in (
+        Elemwise.Mode.TRUE_DIV,
+        Elemwise.Mode.EXP,
+        Elemwise.Mode.POW,
+        Elemwise.Mode.LOG,
+        Elemwise.Mode.EXPM1,
+        Elemwise.Mode.LOG1P,
+        Elemwise.Mode.TANH,
+        Elemwise.Mode.ACOS,
+        Elemwise.Mode.ASIN,
+        Elemwise.Mode.ATAN2,
+        Elemwise.Mode.CEIL,
+        Elemwise.Mode.COS,
+        Elemwise.Mode.FLOOR,
+        Elemwise.Mode.H_SWISH,
+        Elemwise.Mode.ROUND,
+        Elemwise.Mode.SIGMOID,
+        Elemwise.Mode.SIN,
+    ):
+        if mode in (
+            Elemwise.Mode.CEIL,
+            Elemwise.Mode.FLOOR,
+            Elemwise.Mode.ROUND,
+        ) and np.issubdtype(args[0].dtype, np.integer):
+            return args[0]
         args = tuple(map(lambda x: x.astype("float32"), args))
-    _isscalar = True
-    for i in args:
-        if isscalar(i) == False:
-            _isscalar = False
-            break
-    (result,) = apply(op, *args)
-    if _isscalar:
-        setscalar(result)
-    return result
+    return _elwise_apply(args, mode)
 
 
 def _elemwise_multi_type(*args, mode, **kwargs):

imperative/python/test/unit/functional/test_elemwise.py

@@ -9,6 +9,7 @@
 import numpy as np
 
 import megengine.functional as F
+import megengine.functional.elemwise as elemwise
 from megengine import tensor
 from megengine.core.tensor import dtype
 from megengine.functional.elemwise import _elwise
@@ -166,3 +167,20 @@ def test_qadd():
     result_mge = result_mge.astype("float32").numpy()
     result_expect = x.astype("float32").numpy() + y.astype("float32").numpy()
     np.testing.assert_almost_equal(result_mge, result_expect, decimal=6)
+
+
+def test_int32_input():
+    x = tensor(np.array([1, 2, 3, 4, 5]), dtype="int32")
+    for op_name in elemwise.__all__:
+        op = getattr(elemwise, op_name)
+        nargs = op.__code__.co_argcount
+        if op_name == "clip":
+            inp = (x, 0, 1)
+        elif op_name.endswith("_shift"):
+            inp = (x, 1)
+        elif op_name.startswith("logical_"):
+            continue
+        else:
+            inp = (x,) * nargs
+        y = op(*inp)
+        y.numpy()