[Relay] Implement SoftmaxRel for softmax operators.

[wzh99]

This PR fixes #11684. I replace IdentityRel in nn.softmax, nn.fast_softmax and nn.log_softmax with a newly implemented SoftmaxRel so that the attribute axis is checked during type inference. For the test case shown in #11684, the following error is reported:

The axis is not in range [-1, 1)
Traceback (most recent call last):
  File "/Users/wzh/tvm-bug/bug_softmax_axis.py", line 8, in <module>
    mod = relay.transform.InferType()(mod)
  File "/Users/wzh/tvm-dev/python/tvm/ir/transform.py", line 161, in __call__
    return _ffi_transform_api.RunPass(self, mod)
  File "/Users/wzh/tvm-dev/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__
    raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
  [bt] (8) 9   libtvm.dylib                        0x0000000119ef03b4 tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<void tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::transform::Pass, tvm::IRModule)>::AssignTypedLambda<tvm::transform::$_6>(tvm::transform::$_6, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char> >)::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) + 948
  [bt] (7) 8   libtvm.dylib                        0x0000000119ee5964 tvm::transform::Pass::operator()(tvm::IRModule) const + 148
  [bt] (6) 7   libtvm.dylib                        0x0000000119ee5d71 tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const + 753
  [bt] (5) 6   libtvm.dylib                        0x0000000119ee6873 tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const + 819
  [bt] (4) 5   libtvm.dylib                        0x000000011b21ddfd tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<void tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::$_2>(tvm::relay::transform::InferType()::$_2)::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) + 1933
  [bt] (3) 4   libtvm.dylib                        0x000000011b20d217 tvm::relay::TypeInferencer::Infer(tvm::GlobalVar, tvm::relay::Function) + 135
  [bt] (2) 3   libtvm.dylib                        0x000000011afd2a2f tvm::relay::TypeSolver::Solve() + 1615
  [bt] (1) 2   libtvm.dylib                        0x0000000119b86699 tvm::runtime::detail::LogFatal::Entry::Finalize() + 89
  [bt] (0) 1   libtvm.dylib                        0x000000011b5a3508 tvm::runtime::Backtrace() + 24
  [bt] (8) 9   libtvm.dylib                        0x000000011b20d217 tvm::relay::TypeInferencer::Infer(tvm::GlobalVar, tvm::relay::Function) + 135
  [bt] (7) 8   libtvm.dylib                        0x000000011afd285c tvm::relay::TypeSolver::Solve() + 1148
  [bt] (6) 7   libtvm.dylib                        0x000000011afd2dd0 tvm::TypedEnvFunc<bool (tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::operator()(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&) const + 416
  [bt] (5) 6   libtvm.dylib                        0x000000011a08b154 tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<void tvm::runtime::TypedPackedFunc<bool (tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>(bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) + 20
  [bt] (4) 5   libtvm.dylib                        0x000000011a08b563 void tvm::runtime::TypedPackedFunc<bool (tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>::AssignTypedLambda<bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)>(bool (*)(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&))::'lambda'(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)::operator()(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*) const + 1027
  [bt] (3) 4   libtvm.dylib                        0x000000011acd163e tvm::relay::SoftmaxRel(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&) + 942
  [bt] (2) 3   libtvm.dylib                        0x0000000119e6a08b tvm::DiagnosticContext::Render() + 459
  [bt] (1) 2   libtvm.dylib                        0x0000000119b86699 tvm::runtime::detail::LogFatal::Entry::Finalize() + 89
  [bt] (0) 1   libtvm.dylib                        0x000000011b5a3508 tvm::runtime::Backtrace() + 24
  File "/Users/wzh/tvm-dev/src/relay/analysis/type_solver.cc", line 624
TVMError: 
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
  Check failed: (false) is false: [15:01:35] /Users/wzh/tvm-dev/src/ir/diagnostic.cc:105: DiagnosticError: one or more error diagnostics were emitted, please check diagnostic render for output.

[ganler]

A better err msg should indicate the wrong axis. For example,

...
<< "Wrong axis (" << axis << ") not in expected range: [" << -ndim << ", " << ndim << ")";

[wzh99]

I have modified the error message as you suggest.

[ganler]

Basically this PR lets Relay reject invalid softmax operator (axis >= rank) as earlier as the type inference phase (though such invalid cases will be rejected anyhow in later checks). @masahi Can you help decide if we want to merge this improvement? Thanks!