wzh99 opened a new issue, #11684:
URL: https://github.com/apache/tvm/issues/11684
### Expected behavior
The following Relay program should NOT pass type inference:
```
#[version = "0.0.5"]
def @main(%x: Tensor[(4), float32]) {
nn.softmax(%x, axis=1)
}
```
The input tensor `%x` of `nn.softmax` has only one dimension. The valid
range of `axis` is [-1, 1). `axis=1` is obviously invalid in this case.
### Actual behavior
This program passes type inference of Relay.
### Environment
macOS 12.4. Compiled using Clang 13.1.6 with LLVM support. TVM commit
[`0df6961`](https://github.com/apache/tvm/commit/0df69611b2fb46724a0023dd8d389c9a1ecedcb8).
### Steps to reproduce
```python
from tvm import relay, IRModule
x = relay.var('x', shape=(4,))
y = relay.nn.softmax(x, axis=1)
mod = IRModule.from_expr(y)
mod = relay.transform.InferType()(mod)
```
### Possible fix
https://github.com/apache/tvm/blob/0df69611b2fb46724a0023dd8d389c9a1ecedcb8/src/relay/op/nn/nn.cc#L409-L423
In operator registration of `nn.softmax`, its type relation is set to be
`IdentityRel`. However, `nn.softmax` has an attribute `axis` that is not
checked by `IdentityRel`.
A possible fix is to implement a new type relation function that checks
`axis` attribute in `SoftmaxAttrs`. This type relation also applies to
`nn.fast_softmax` and `nn.log_softmax`.
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