t-vi commented on a change in pull request #6449:
URL: https://github.com/apache/incubator-tvm/pull/6449#discussion_r487271315
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File path: python/tvm/relay/frontend/pytorch.py
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@@ -127,8 +128,22 @@ def _is_quantized_tensor(data, prelude):
# operator implementation
def _elemwise(name):
def _impl(inputs, input_types):
- data0, data1 = _pytorch_promote_types(inputs[:2], input_types[:2])
- return get_relay_op(name)(data0, data1)
+ dtype0, dtype1 = input_types[:2]
+ if isinstance(inputs[0], _expr.Expr):
+ dtype0 = _infer_type(inputs[0]).checked_type.dtype
+ if isinstance(inputs[1], _expr.Expr):
+ dtype1 = _infer_type(inputs[1]).checked_type.dtype
+
Review comment:
I think we would eventually want to look at using type propagation more.
However, the issue here is that PyTorch's default dtype for integral tensors
is int64. I don't think we should be hacking around that, really, because we're
bound to end up with cases where int64 is the right thing to have. If I
understood the discussions on the forum correctly, the idea was to downcast 64
bit indexing to 32 based if it is considered safe.
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