t-vi commented on a change in pull request #5834:
URL: https://github.com/apache/incubator-tvm/pull/5834#discussion_r442079677
##########
File path: python/tvm/relay/frontend/pytorch.py
##########
@@ -115,64 +115,70 @@ def inplace_add_to_add(op_name):
return False
+
# operator implementation
def _elemwise(name):
def _impl(inputs, input_types):
- # TODO: Figure out a better way to get typing to work for tensor +
scalar
- type0 = input_types[0]
- if isinstance(inputs[1], _expr.Expr):
- type0 = input_types[1]
-
- type1 = input_types[1]
- if isinstance(inputs[0], _expr.Expr):
- type1 = input_types[0]
-
- data0 = _convert_elemwise_input(inputs[0], type0)
- data1 = _convert_elemwise_input(inputs[1], type1)
-
+ data0, data1 = _pytorch_promote_types(inputs[:2], input_types[:2])
Review comment:
So the immediate error was caused by numpy scalars instead of Python
number. :confused:
That is easily fixed. I do bump into something about the shape in a where
statement not being broadcast. That's probably unrelated to the dtype fixes,
but I can throw in broadcasting for where (the question being whether we want
it in relay, too, or just in the frontend).
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