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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/03/09 22:22:18 UTC
[GitHub] [incubator-tvm] trevor-m opened a new pull request #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
trevor-m opened a new pull request #5018: Look for TupleType instead of TupleNode in LayoutRewriter
URL: https://github.com/apache/incubator-tvm/pull/5018
When getting the input shapes from the args LayoutRewriter checked for tuples only by seeing if the node is an instance of TupleNOde. With that code, the following relay program would encounter an error during LayoutRewriter because the arg is a relay.Var but has a TupleTypeNode instead of a TensorTypeNode as it expected.
```
def @main(%input_0: Tensor[(1, 2, 6, 6), float32], %input_1: Tensor[(1, 3, 6, 6), float32]) -> Tensor[(1, 5, 6, 6), float32] {
%0 = (%input_0, %input_1);
%1 = fn (%x: (Tensor[(1, 2, 6, 6), float32], Tensor[(1, 3, 6, 6), float32])) -> Tensor[(1, 5, 6, 6), float32] {
concatenate(%x, axis=1) /* ty=Tensor[(1, 5, 6, 6), float32] */
};
%1(%0) /* ty=Tensor[(1, 5, 6, 6), float32] */
}
```
@zhiics @anijain2305 Could you please review?
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[GitHub] [incubator-tvm] trevor-m closed pull request #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
trevor-m closed pull request #5018: Look for TupleType instead of TupleNode in LayoutRewriter
URL: https://github.com/apache/incubator-tvm/pull/5018
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[GitHub] [incubator-tvm] trevor-m edited a comment on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [incubator-tvm] trevor-m commented on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by 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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[GitHub] [incubator-tvm] anijain2305 commented on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
anijain2305 commented on issue #5018: Look for TupleType instead of TupleNode in LayoutRewriter
URL: https://github.com/apache/incubator-tvm/pull/5018#issuecomment-599183145
https://github.com/apache/incubator-tvm/pull/5066
Above PR have same line changes. Might be a good idea to try that first.
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[GitHub] [incubator-tvm] tqchen commented on issue #5018: Look for TupleType
instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
tqchen commented on issue #5018: Look for TupleType instead of TupleNode in LayoutRewriter
URL: https://github.com/apache/incubator-tvm/pull/5018#issuecomment-606245916
@trevor-m @anijain2305 @zhiics please followup
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[GitHub] [incubator-tvm] trevor-m edited a comment on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
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
```
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[GitHub] [incubator-tvm] trevor-m commented on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by 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-606272480
Closing since https://github.com/apache/incubator-tvm/pull/5066 also solves this issue.
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[GitHub] [incubator-tvm] anijain2305 commented on issue #5018: Look for
TupleType instead of TupleNode in LayoutRewriter
Posted by GitBox <gi...@apache.org>.
anijain2305 commented on issue #5018: Look for TupleType instead of TupleNode in LayoutRewriter
URL: https://github.com/apache/incubator-tvm/pull/5018#issuecomment-596832368
Trevor, can you please add a test case? The implementation is good, but we need a test case.
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