[GitHub] [incubator-tvm] trevor-m commented on issue #5018: Look for TupleType instead of TupleNode in LayoutRewriter

[GitBox <gi...@apache.org>]

trevor-m commented 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.
   ```
   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)
   ```
   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
   ```

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