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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/09/15 02:58:59 UTC

[GitHub] [tvm] areusch opened a new issue #9013: [Bug] docs do not build from a clean source tree

areusch opened a new issue #9013:
URL: https://github.com/apache/tvm/issues/9013


   I think this might be a compiler caching bug cc @tqchen @jroesch @mbs-octoml . Not caught in the regression because the regression rebuilds only changed tutorials.
   
   Steps to reproduce:
   1. `git checkout dc2f70e3c8a9b14b9e414ecf768ad32e6c3c3960`
   2. `rm -rf build`
   3. `docker/bash.sh ci_gpu tests/scripts/task_config_build_gpu.sh`
   4. `docker/bash.sh ci_gpu tests/scripts/task_build.sh build -j16`
   5. `docker/bash.sh ci_gpu bash -c 'cd docs && make clean'`
   6. `docker/bash.sh ci_gpu tests/scripts/task_ci_setup.sh`
   7. `docker/bash.sh ci_gpu tests/scripts/task_python_docs.sh`
   
   Will show this traceback somewhere along the way. micro_autotune was just trying to build a relay model. I _think_ the shapes look correct to me.
   
   ```
   conv2d: requires that `0`, the input channels (0) divided by groups (1),                                                                                                          [1324/4555]
    must match the input channels of the weight `3`, where the weight shape is ([6, 3, 5, 5]).
   The type inference pass was unable to infer a type for this expression.
   This usually occurs when an operator call is under constrained in some way, check other reported errors for hints of what may of happened.
   WARNING: /home/areusch/ws/tvm4/tutorials/micro/micro_autotune.py failed to execute correctly: Traceback (most recent call last):
     File "/usr/local/lib/python3.6/dist-packages/sphinx_gallery/gen_rst.py", line 480, in _memory_usage
       out = func()
     File "/usr/local/lib/python3.6/dist-packages/sphinx_gallery/gen_rst.py", line 465, in __call__
       exec(self.code, self.globals)
     File "/home/areusch/ws/tvm4/tutorials/micro/micro_autotune.py", line 179, in <module>
       lowered = tvm.relay.build(relay_mod, target=TARGET, params=params)
     File "../../python/tvm/relay/build_module.py", line 358, in build
       mod=ir_mod, target=target, params=params, executor=executor, mod_name=mod_name
     File "../../python/tvm/relay/build_module.py", line 172, in build
       self._build(mod, target, target_host, executor, mod_name)
     File "tvm/_ffi/_cython/./packed_func.pxi", line 323, in tvm._ffi._cy3.core.PackedFuncBase.__call__
     File "tvm/_ffi/_cython/./packed_func.pxi", line 267, in tvm._ffi._cy3.core.FuncCall
     File "tvm/_ffi/_cython/./base.pxi", line 163, in tvm._ffi._cy3.core.CALL
   tvm.error.DiagnosticError: Traceback (most recent call last):
     27: TVMFuncCall
           at /home/areusch/ws/tvm4/src/runtime/c_runtime_api.cc:474
     26: tvm::runtime::PackedFunc::CallPacked(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1151
     25: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
           at /usr/include/c++/7/bits/std_function.h:706
     24: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::relay::backend::RelayBuildModule::GetFunction(std::__cxx11::basic_string<char, std::char_traits<c
   har>, std::allocator<char> > const&, tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#3}>::_M_invoke(std::_Any_data const&,
   tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
           at /usr/include/c++/7/bits/std_function.h:316
     23: tvm::relay::backend::RelayBuildModule::GetFunction(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::ObjectPtr<tvm::runtime::Object
   > const&)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#3}::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
           at /home/areusch/ws/tvm4/src/relay/backend/build_module.cc:181
     22: tvm::relay::backend::RelayBuildModule::Build(tvm::IRModule, tvm::runtime::Map<tvm::Integer, tvm::Target, void, void> const&, tvm::Target const&, tvm::runtime::String, tvm::runtime::St
   ring)
           at /home/areusch/ws/tvm4/src/relay/backend/build_module.cc:288
     21: tvm::relay::backend::RelayBuildModule::BuildRelay(tvm::IRModule, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tvm::runtime::NDAr
   ray, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
    > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, tvm::runtime::NDArray> > > const&, tvm::runtime::String)
           at /home/areusch/ws/tvm4/src/relay/backend/build_module.cc:479
     20: tvm::relay::backend::RelayBuildModule::Optimize(tvm::IRModule, tvm::runtime::Map<tvm::Integer, tvm::Target, void, void> const&, std::unordered_map<std::__cxx11::basic_string<char, std
   ::char_traits<char>, std::allocator<char> >, tvm::runtime::NDArray, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::
   basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, tvm::run
   time::NDArray> > > const&)
           at /home/areusch/ws/tvm4/src/relay/backend/build_module.cc:329
     19: tvm::transform::Pass::operator()(tvm::IRModule) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:255
     18: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:267
     17: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:481
     16: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
          at /home/areusch/ws/tvm4/src/ir/transform.cc:267
     15: tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
           at /home/areusch/ws/tvm4/src/relay/ir/transform.cc:160
     14: tvm::transform::Pass::operator()(tvm::IRModule) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:255
     13: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:267
     12: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
           at /home/areusch/ws/tvm4/src/ir/transform.cc:415
     11: tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::operator()(tvm::IRModule, tvm::transform::PassContext) const
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1498
     10: tvm::IRModule tvm::runtime::detail::typed_packed_call_dispatcher<tvm::IRModule>::run<tvm::IRModule, tvm::transform::PassContext>(tvm::runtime::PackedFunc const&, tvm::IRModule&&, tvm:
   :transform::PassContext&&)
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1444
     9: tvm::runtime::TVMRetValue tvm::runtime::PackedFunc::operator()<tvm::IRModule, tvm::transform::PassContext>(tvm::IRModule&&, tvm::transform::PassContext&&) const
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1369
     8: std::function<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)>::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const
           at /usr/include/c++/7/bits/std_function.h:706
     7: _M_invoke
           at /usr/include/c++/7/bits/std_function.h:316
     6: operator()
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1492
     5: unpack_call<tvm::IRModule, 2, tvm::relay::transform::InferType()::<lambda(tvm::IRModule, const PassContext&)> >
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1421
     4: run<>
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1382
     3: run<tvm::runtime::TVMMovableArgValueWithContext_>
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1382
     2: run<tvm::runtime::TVMMovableArgValueWithContext_, tvm::runtime::TVMMovableArgValueWithContext_>
           at /home/areusch/ws/tvm4/include/tvm/runtime/packed_func.h:1397
     1: operator()
           at /home/areusch/ws/tvm4/src/relay/transforms/type_infer.cc:857
     0: tvm::DiagnosticContext::Render()
           at /home/areusch/ws/tvm4/src/ir/diagnostic.cc:105
     File "/home/areusch/ws/tvm4/src/ir/diagnostic.cc", line 105
   ```


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923447136


   No luck on a local repro (using nvidia docker and the ci_gpu image) as nvcc.py is failing for some reason.


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924418401


   The AlterOpLayout pass is behaving differently.
   
   On the GOOD runs:
   ```
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW3c");
     %1 = layout_transform(meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, src_layout="OIHW", dst_layout="OIHW3i3o");
     %2 = nn.contrib_conv2d_NCHWc(%0, %1, padding=[2, 2, 2, 2], channels=6, kernel_size=[5, 5], data_layout="NCHW3c", kernel_layout="OIHW3i3o", out_layout="NCHW3c", out_dtype="float32");
     layout_transform(%2, src_layout="NCHW3c", dst_layout="NCHW")
   }
   ```
   
   On the BAD runs:
   ```
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:134: AlterOpLayout: Executing function pass with opt level: 3
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW16c");
     %1 = nn.conv2d(%0, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], data_layout="NCHW16c", out_dtype="float32");
     layout_transform(%1, src_layout="NCHW16c", dst_layout="NCHW")
   }
   ```


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[GitHub] [tvm] jroesch closed issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
jroesch closed issue #9013:
URL: https://github.com/apache/tvm/issues/9013


   


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[GitHub] [tvm] zxybazh edited a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
zxybazh edited a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923685122


   Same here in my PR's CI. #9053 


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[GitHub] [tvm] mikepapadim edited a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mikepapadim edited a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923760949


   I had the issue also in the stagging Jenkins. 


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[GitHub] [tvm] zxybazh commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
zxybazh commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924422920


   So the only difference is `dst_layout` of `NCHW3c` v.s. `NCHW16c`?


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924425963


   As expected all is well if disable AlterOpLayout. I need to log whatever hidden state is driving that rewrite.


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[GitHub] [tvm] mbs-octoml edited a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml edited a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924421507


   On the BAD runs:
   ```
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW16c");
     %1 = nn.conv2d(%0, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], data_layout="NCHW16c", out_dtype="float32");
     layout_transform(%1, src_layout="NCHW16c", dst_layout="NCHW")
   }
   ```
   
   I can't find any matching rewrite on the GOOD runs -- they're all of the form:
   ```
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW3c");
     %1 = layout_transform(meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, src_layout="OIHW", dst_layout="OIHW3i3o");
     %2 = nn.contrib_conv2d_NCHWc(%0, %1, padding=[2, 2, 2, 2], channels=6, kernel_size=[5, 5], data_layout="NCHW3c", kernel_layout="OIHW3i3o", out_layout="NCHW3c", out_dtype="float32");
     layout_transform(%2, src_layout="NCHW3c", dst_layout="NCHW")
   }
   ```
   
   Eh?


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[GitHub] [tvm] areusch commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
areusch commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923406795


   Failed again in mainline CI: https://ci.tlcpack.ai/blue/organizations/jenkins/tvm/detail/PR-9050/1/pipeline
   


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-920129733


   @electriclilies Lily, in your spelunking through build did you see any obvious global compile engine caching?


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923536637


   I can repro with my local config just with make clean & make html, no need for docker etc. Good, that's easier.


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[GitHub] [tvm] zxybazh commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
zxybazh commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923685122


   Same here in my CI. #9053 


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924490482


   So the root problem is our tutorials need to be hermetic but there's no 'unregister' mechanism or ability to register under some 'with TvmRegistrationScope()' statement. 
   
   At least making that layout xform valid will let us hobble along a bit longer tho.


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924485483


   Ok after getting lost in AlterOpLayout I see dev/use_pass_infra.py has @relay.op.register_alter_op_layout("nn.conv2d") which is obviously sticky and still visibible to the later micro_autotune.py. Almost certainly that defn is ill-formed in some way.


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[GitHub] [tvm] areusch commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
areusch commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-925096671


   thanks for the detailed investigation @mbs-octoml ! I do think we should make the compiler work multiple times in a row. certainly our unit tests require this and we will expose a bunch of problems with xdist after it starts reordering them. :)


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-926175108


   This is fixed -- don't have edit rights on issues.


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[GitHub] [tvm] mikepapadim commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mikepapadim commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923760949


   I had the issue also in the stagging Jenkins last week. 


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[GitHub] [tvm] jroesch commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
jroesch commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-926192284


   Thanks for fixing this one @mbs-octoml !


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924379842


   I'm looking again now.


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[GitHub] [tvm] mbs-octoml removed a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml removed a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924449025


   It seems to be a dangling ref issue but let me understand exactly what was going wrong since there's a lot of closure copying going on. It at least passes with my 'fix' but I also have a clang sliced-cast warning firing which is very strange.


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[GitHub] [tvm] mbs-octoml removed a comment on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml removed a comment on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924418401


   The AlterOpLayout pass is behaving differently.
   
   On the GOOD runs:
   ```
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW3c");
     %1 = layout_transform(meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, src_layout="OIHW", dst_layout="OIHW3i3o");
     %2 = nn.contrib_conv2d_NCHWc(%0, %1, padding=[2, 2, 2, 2], channels=6, kernel_size=[5, 5], data_layout="NCHW3c", kernel_layout="OIHW3i3o", out_layout="NCHW3c", out_dtype="float32");
     layout_transform(%2, src_layout="NCHW3c", dst_layout="NCHW")
   }
   ```
   
   On the BAD runs:
   ```
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:134: AlterOpLayout: Executing function pass with opt level: 3
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW16c");
     %1 = nn.conv2d(%0, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], data_layout="NCHW16c", out_dtype="float32");
     layout_transform(%1, src_layout="NCHW16c", dst_layout="NCHW")
   }
   ```


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-925108438


   https://github.com/apache/tvm/pull/9076


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924490816


   Even better- stop using sphinx_gallery.


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924449025


   It seems to be a dangling ref issue but let me understand exactly what was going wrong since there's a lot of closure copying going on. It at least passes with my 'fix' but I also have a clang sliced-cast warning firing which is very strange.


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[GitHub] [tvm] areusch commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
areusch commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-923488268


   @mbs-octoml i think you can repro like this:
   1. `git checkout 44d3934be5d33590ba63139f9b756b05aec9d5c5`
   2. `rm -rf build`
   3. `docker/bash.sh ci_gpu tests/scripts/task_config_build_gpu.sh`
   4. `docker/bash.sh ci_gpu tests/scripts/task_build.sh build -j16`  # note: adjust `-j16` on your box
   5. `docker/bash.sh -it ci_gpu bash -c 'cd docs && make clean && TVM_TUTORIAL_EXEC_PATTERN="(micro)|(dev/use_pass_infra)" make html'`
   
   it definitely will not repro if you've already built the docs once and don't run `cd docs && make clean`.


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[GitHub] [tvm] mbs-octoml commented on issue #9013: [Bug] tutorials do not build from a clean source tree

Posted by GitBox <gi...@apache.org>.
mbs-octoml commented on issue #9013:
URL: https://github.com/apache/tvm/issues/9013#issuecomment-924421507


   On the BAD runs:
   ```
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:56:21] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW16c");
     %1 = nn.conv2d(%0, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], data_layout="NCHW16c", out_dtype="float32");
     layout_transform(%1, src_layout="NCHW16c", dst_layout="NCHW")
   }
   ```
   
   I can't find any matching rewrite on the good runs -- they're all of the form:
   ```
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:135: AlterOpLayout: Input module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, padding=[2, 2, 2, 2], kernel_size=[5, 5], out_dtype="float32") /* ty=Tensor[(1, 6, 10, 10), float32] */
   }
   
   
   [14:53:31] /home/mbs/github/mbs-tvm/src/relay/ir/transform.cc:159: AlterOpLayout: Output module:
   def @main(%data: Tensor[(1, 3, 10, 10), float32]) -> Tensor[(1, 6, 10, 10), float32] {
     %0 = layout_transform(%data, src_layout="NCHW", dst_layout="NCHW3c");
     %1 = layout_transform(meta[relay.Constant][0] /* ty=Tensor[(6, 3, 5, 5), float32] */, src_layout="OIHW", dst_layout="OIHW3i3o");
     %2 = nn.contrib_conv2d_NCHWc(%0, %1, padding=[2, 2, 2, 2], channels=6, kernel_size=[5, 5], data_layout="NCHW3c", kernel_layout="OIHW3i3o", out_layout="NCHW3c", out_dtype="float32");
     layout_transform(%2, src_layout="NCHW3c", dst_layout="NCHW")
   }
   ```
   
   Eh?


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