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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/01/24 09:08:37 UTC
[GitHub] [tvm] Pony23333 opened a new issue #10042: [Bug] Dynamic Varsize error when tune detection model with autotvm
Pony23333 opened a new issue #10042:
URL: https://github.com/apache/tvm/issues/10042
Hi all,
I am a beginner of TVM and I found it's suppressingly fast when deploy on mobile devices compared with other frameworks like ncnn.
However, when I tried to tune a detection model ([YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.onnx)) with autotvm on PC's CPU, I got a type error from converting Virtual axis. The demo I followed is from https://tvm.apache.org/docs/reference/api/python/autotvm.html and the error comes from the code:
`tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params)`
I guess the error is from the resize operator used in FPN for detection models. But even I fixed all the input shapes, there still are dynamic axis size (any_dim: int32).
I wonder if there is a solution or workaround. Thank you.
The TVM I use is of version '0.9.dev0'.
The error log is as below:
Exception in thread Thread-1:
Traceback (most recent call last):
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/threading.py", line 932, in _bootstrap_inner
self.run()
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/relay_integration.py", line 55, in _lower
compiler.lower(mod, target=target)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/relay/backend/vm.py", line 155, in lower
self._lower(mod, target, target_host)
File "tvm/_ffi/_cython/./packed_func.pxi", line 323, in tvm._ffi._cy3.core.PackedFuncBase.__call__
File "tvm/_ffi/_cython/./packed_func.pxi", line 257, in tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./packed_func.pxi", line 246, in tvm._ffi._cy3.core.FuncCall3
File "tvm/_ffi/_cython/./base.pxi", line 163, in tvm._ffi._cy3.core.CALL
tvm._ffi.base.TVMError: Traceback (most recent call last):
48: TVMFuncCall
47: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::relay::vm::VMCompiler::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*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
46: tvm::relay::vm::VMCompiler::Lower(tvm::IRModule, tvm::runtime::Map<tvm::Integer, tvm::Target, void, void>, tvm::Target)
45: tvm::relay::vm::VMCompiler::OptimizeModuleImpl(tvm::IRModule)
44: tvm::transform::Pass::operator()(tvm::IRModule) const
43: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
42: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
41: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
40: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
39: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
38: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
37: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::tec::LowerTEPass(tvm::runtime::String const&, std::function<void (tvm::BaseFunc)>, tvm::VirtualDevice)::{lambda(tvm::IRModule, tvm::transform::PassContext)#1}>(tvm::relay::tec::LowerTEPass(tvm::runtime::String const&, std::function<void (tvm::BaseFunc)>, tvm::VirtualDevice)::{lambda(tvm::IRModule, tvm::transform::PassContext)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
36: tvm::relay::tec::LowerTE(tvm::IRModule const&, tvm::runtime::String const&, std::function<void (tvm::BaseFunc)>, tvm::VirtualDevice)
35: tvm::transform::Pass::operator()(tvm::IRModule) const
34: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
33: tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
32: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::relay::Function (tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::tec::LowerTensorExpr(tvm::runtime::String const&, tvm::relay::tec::TECompiler, std::function<void (tvm::BaseFunc)>, tvm::VirtualDevice)::{lambda(tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)#1}>(tvm::relay::tec::LowerTensorExpr(tvm::runtime::String const&, tvm::relay::tec::TECompiler, std::function<void (tvm::BaseFunc)>, tvm::VirtualDevice)::{lambda(tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
31: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
30: _ZZN3tvm5relay11ExprFunc
29: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::FunctionNode const*)
28: tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::FunctionNode const*)
27: _ZN3tvm5relay9
26: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::FunctionNode const*)
25: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
24: _ZZN3tvm5relay11ExprFunc
23: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::LetNode const*)
22: tvm::relay::tec::LowerTensorExprMutator::PreVisitLetBinding_(tvm::relay::Var const&, tvm::RelayExpr const&)
21: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
20: _ZZN3tvm5relay11ExprFunc
19: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::CallNode const*)
18: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
17: _ZZN3tvm5relay11ExprFunc
16: tvm::relay::transform::DeviceAwareExprMutator::VisitExpr_(tvm::relay::CallNode const*)
15: tvm::relay::tec::LowerTensorExprMutator::DeviceAwareVisitExpr_(tvm::relay::CallNode const*)
14: tvm::relay::tec::LowerTensorExprMutator::MakeLoweredCall(tvm::relay::Function, tvm::runtime::Array<tvm::RelayExpr, void>, tvm::Span, tvm::Target)
13: tvm::relay::tec::TECompilerImpl::Lower(tvm::relay::tec::CCacheKey const&, tvm::runtime::String)
12: tvm::relay::tec::TECompilerImpl::LowerInternal(tvm::relay::tec::CCacheKey const&, std::function<tvm::runtime::String (tvm::runtime::String)>)
11: tvm::relay::tec::PrimFuncFor(tvm::relay::Function const&, tvm::Target const&, std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > (std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)>)
10: tvm::relay::tec::ScheduleBuilder::Create(tvm::relay::Function const&, std::function<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > (std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)>)
9: tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&)
8: _ZZN3tvm5relay11ExprFunc
7: tvm::relay::tec::ScheduleBuilder::VisitExpr_(tvm::relay::CallNode const*)
6: tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&)
5: _ZZN3tvm5relay11ExprFunc
4: tvm::relay::tec::ScheduleBuilder::VisitExpr_(tvm::relay::CallNode const*)
3: tvm::relay::backend::MemoizedExprTranslator<tvm::runtime::Array<tvm::te::Tensor, void> >::VisitExpr(tvm::RelayExpr const&)
2: _ZZN3tvm5relay11ExprFunc
1: tvm::relay::tec::ScheduleBuilder::VisitExpr_(tvm::relay::CallNode const*)
0: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), TVMFuncCreateFromCFunc::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#2}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/relay/backend/te_compiler.py", line 314, in lower_call
best_impl, outputs = select_implementation(
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/relay/backend/te_compiler.py", line 189, in select_implementation
outs = best_plevel_impl.compute(attrs, inputs, out_type)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/relay/op/op.py", line 126, in compute
return _OpImplementationCompute(self, attrs, inputs, out_type)
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
3: TVMFuncCall
2: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::relay::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#4}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
1: tvm::relay::OpImplementation::Compute(tvm::Attrs const&, tvm::runtime::Array<tvm::te::Tensor, void> const&, tvm::Type const&)
0: std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), TVMFuncCreateFromCFunc::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#2}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
File "tvm/_ffi/_cython/./packed_func.pxi", line 56, in tvm._ffi._cy3.core.tvm_callback
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/relay/op/strategy/generic.py", line 243, in _compute_conv2d
return [topi_compute(*args)]
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/topi/x86/conv2d.py", line 129, in conv2d_nchw
packed_out = conv2d_NCHWc(data, kernel, strides, padding, dilation, layout, layout, out_dtype)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/topi_integration.py", line 165, in wrapper
node = topi_compute(cfg, *args)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/topi/x86/conv2d.py", line 194, in conv2d_NCHWc
cfg.define_split("tile_ic", in_channel, num_outputs=2)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 731, in define_split
return self._add_new_transform(SplitSpace, name, axes, policy, **kwargs)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 834, in _add_new_transform
axes = [x if isinstance(x, (VirtualAxis, Axis)) else self.axis(x) for x in axes]
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 834, in <listcomp>
axes = [x if isinstance(x, (VirtualAxis, Axis)) else self.axis(x) for x in axes]
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 688, in axis
return VirtualAxis(var)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 142, in __init__
raise RuntimeError("Invalid type of axis: " + str(type(var)))
RuntimeError: Invalid type of axis: <class 'tvm.tir.expr.SizeVar'>
any_dim: int32
Traceback (most recent call last):
File "autotvm_relay_x86.py", line 233, in <module>
tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/relay_integration.py", line 87, in extract_from_program
return extract_from_multiple_program([mod], [params], target, ops=ops)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/relay_integration.py", line 153, in extract_from_multiple_program
tsk = create(task_name, args, target=target)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/task.py", line 485, in create
sch, _ = ret.func(*args)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/task.py", line 240, in __call__
return self._default_func(*args, **kwargs)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/task.py", line 246, in _default_func
out = self.fcompute(*args, **kwargs)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/topi_integration.py", line 165, in wrapper
node = topi_compute(cfg, *args)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/topi/x86/conv2d.py", line 194, in conv2d_NCHWc
cfg.define_split("tile_ic", in_channel, num_outputs=2)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 731, in define_split
return self._add_new_transform(SplitSpace, name, axes, policy, **kwargs)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 834, in _add_new_transform
axes = [x if isinstance(x, (VirtualAxis, Axis)) else self.axis(x) for x in axes]
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 834, in <listcomp>
axes = [x if isinstance(x, (VirtualAxis, Axis)) else self.axis(x) for x in axes]
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 688, in axis
return VirtualAxis(var)
File "/home/jtma3/anaconda3/envs/nanodet/lib/python3.8/site-packages/tvm-0.9.dev369+g24267492d-py3.8-linux-x86_64.egg/tvm/autotvm/task/space.py", line 142, in __init__
raise RuntimeError("Invalid type of axis: " + str(type(var)))
RuntimeError: Invalid type of axis: <class 'tvm.tir.expr.SizeVar'>
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[GitHub] [tvm] Pony23333 commented on issue #10042: [Bug] Dynamic Varsize error when tune detection model with autotvm
Posted by GitBox <gi...@apache.org>.
Pony23333 commented on issue #10042:
URL: https://github.com/apache/tvm/issues/10042#issuecomment-1022787390
There is a workaround for this problem: use torchscript as frontend instead of onnx
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