jwfromm commented on a change in pull request #4961: [Torch] Upsampling op
support and enable registering a user defined op conversion map
URL: https://github.com/apache/incubator-tvm/pull/4961#discussion_r385830597
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -614,6 +615,51 @@ def _impl(inputs, input_types):
return _op.tensor.sqrt(data)
return _impl
+def _floor():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.floor(data)
+ return _impl
+
+def _to():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ # special handling for aten::to(data, 6, _, _, _) case
+ # 6 means dtype = float
+ # this happens when converting upsampling with scale factor
+ cast_func = {
+ 6: float,
+ }
+ if inputs[1] in cast_func and not isinstance(data, _expr.Expr):
Review comment:
This seems very specific, it looks like it only works for non expression
inputs and casts to floats. Is there any reason not to add a `relay.cast`
fallback for expression inputs and support other datatypes in the cast_func
lookup?
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