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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/06/15 07:10:12 UTC
[GitHub] [tvm] wzh99 opened a new pull request, #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
wzh99 opened a new pull request, #11728:
URL: https://github.com/apache/tvm/pull/11728
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 be 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.
```
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[GitHub] [tvm] ganler commented on a diff in pull request #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
Posted by GitBox <gi...@apache.org>.
ganler commented on code in PR #11728:
URL: https://github.com/apache/tvm/pull/11728#discussion_r897705134
##########
src/relay/op/nn/nn.cc:
##########
@@ -399,6 +399,27 @@ where :math:`*` is an channelwise multiplication for each sample in the batch.
// relay.softmax
TVM_REGISTER_NODE_TYPE(SoftmaxAttrs);
+bool SoftmaxRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 2);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) return false;
+
+ const SoftmaxAttrs* param = attrs.as<SoftmaxAttrs>();
+ ICHECK(param != nullptr);
+ int axis = param->axis;
+ int ndim = static_cast<int>(data->shape.size());
+ if (axis >= ndim || axis < -ndim) {
+ reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
+ << "The axis is not in range [" << -ndim << ", " << ndim
Review Comment:
A better err msg should indicate the wrong axis. For example,
```c++
...
<< "Wrong axis (" << axis << ") not in expected range: [" << -ndim << ", " << ndim << ")";
```
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[GitHub] [tvm] masahi merged pull request #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
Posted by GitBox <gi...@apache.org>.
masahi merged PR #11728:
URL: https://github.com/apache/tvm/pull/11728
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[GitHub] [tvm] ganler commented on a diff in pull request #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
Posted by GitBox <gi...@apache.org>.
ganler commented on code in PR #11728:
URL: https://github.com/apache/tvm/pull/11728#discussion_r898283093
##########
src/relay/op/nn/nn.cc:
##########
@@ -399,6 +399,27 @@ where :math:`*` is an channelwise multiplication for each sample in the batch.
// relay.softmax
TVM_REGISTER_NODE_TYPE(SoftmaxAttrs);
+bool SoftmaxRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 2);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) return false;
+
+ const SoftmaxAttrs* param = attrs.as<SoftmaxAttrs>();
+ ICHECK(param != nullptr);
+ int axis = param->axis;
+ int ndim = static_cast<int>(data->shape.size());
+ if (axis >= ndim || axis < -ndim) {
+ reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
+ << "The axis is not in range [" << -ndim << ", " << ndim
Review Comment:
It seems the value of variable 'axis' still has not been printed?
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[GitHub] [tvm] wzh99 commented on a diff in pull request #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
Posted by GitBox <gi...@apache.org>.
wzh99 commented on code in PR #11728:
URL: https://github.com/apache/tvm/pull/11728#discussion_r897888041
##########
src/relay/op/nn/nn.cc:
##########
@@ -399,6 +399,27 @@ where :math:`*` is an channelwise multiplication for each sample in the batch.
// relay.softmax
TVM_REGISTER_NODE_TYPE(SoftmaxAttrs);
+bool SoftmaxRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 2);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) return false;
+
+ const SoftmaxAttrs* param = attrs.as<SoftmaxAttrs>();
+ ICHECK(param != nullptr);
+ int axis = param->axis;
+ int ndim = static_cast<int>(data->shape.size());
+ if (axis >= ndim || axis < -ndim) {
+ reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
+ << "The axis is not in range [" << -ndim << ", " << ndim
Review Comment:
I have modified the error message as you suggest.
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[GitHub] [tvm] ganler commented on pull request #11728: [Relay] Implement `SoftmaxRel` for softmax operators.
Posted by GitBox <gi...@apache.org>.
ganler commented on PR #11728:
URL: https://github.com/apache/tvm/pull/11728#issuecomment-1157014075
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!
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