trevor-m edited a comment on issue #5018: Look for TupleType instead of TupleNode in LayoutRewriter URL: https://github.com/apache/incubator-tvm/pull/5018#issuecomment-597257018 > IMHO, I don't think we need to test this functionality in the partitioning pass as it is a unit test of layout itself. Instead, You can directly make the "partitioned" graph and run Alterlayout pass and assert whatever IR it should emit. Hi Zhi, I don't think it is possible to create the partitioned graph without actually using partitoning. I recreated the subgraph below: ``` input_type = relay.TensorType((1, 5, 6, 6), "float32") x = relay.var("x", relay.TupleType([input_type, input_type])) out = relay.concatenate(x, axis=1) func = relay.Function([x], out) ``` However, the `relay.concatenate` python API does not accept a Var node as the input: ``` File "test_pass_alter_op_layout.py", line 1053, in test_concatenate out = relay.concatenate(x, axis=1) File "/data/neo-ai-tvm/python/tvm/relay/op/tensor.py", line 888, in concatenate data = list(data) TypeError: 'Var' object is not iterable ``` Edit: I was able to bypass this by calling `tvm.relay.op._make.concatenate`. Updating PR...
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