[Bug] Graph optimization model compilation error involving Pad operator

[shaoyuyoung]

I am trying to compile an ONNX (graph below) model using TVM.

Of course, this is a complicated graph, but we can simplify it as below.

These two graphs are equal. When I try to compile them using TVM. The original ONNX model fails but the simplified ONNX model passes. It is very strange!

This seems to involve the Pad operator shape-checking problem.

In theory, I think TVM should have strong compatibility with the native ONNX model. However, the truth is not satisfactory.

It seems that only simplified, simple models are acceptable to TVM

Expected behavior

ONNX compilation passes

Actual behavior

onnx fail
Traceback (most recent call last):
  18: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::transform::Pass, tvm::IRModule)>::AssignTypedLambda<tvm::transform::__mk_TVM9::{lambda(tvm::transform::Pass, tvm::IRModule)#1}>(tvm::transform::__mk_TVM9::{lambda(tvm::transform::Pass, tvm::IRModule)#1}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, tvm::runtime::TVMRetValue)
  17: tvm::transform::Pass::operator()(tvm::IRModule) const
  16: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  15: tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  14: _ZN3tvm7runtime13PackedFun
  13: tvm::runtime::TypedPackedFunc<tvm::relay::Function (tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::DynamicToStatic()::{lambda(tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)#1}>(tvm::relay::transform::DynamicToStatic()::{lambda(tvm::relay::Function, tvm::IRModule, tvm::transform::PassContext)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*) const
  12: tvm::relay::DynamicToStatic(tvm::relay::Function, tvm::IRModule)
  11: tvm::relay::DynamicToStaticMutator::PrepareInput(tvm::RelayExpr const&)
  10: tvm::transform::Pass::operator()(tvm::IRModule) const
  9: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  8: tvm::relay::transform::FunctionPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  7: tvm::transform::Pass::operator()(tvm::IRModule) const
  6: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  5: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
  4: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1}>(tvm::relay::transform::InferType()::{lambda(tvm::IRModule, tvm::transform::PassContext const&)#1})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
  3: tvm::relay::TypeInferencer::Infer(tvm::GlobalVar, tvm::relay::Function)
  2: tvm::relay::TypeSolver::Solve()
  1: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<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*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
  0: tvm::relay::PadRel(tvm::runtime::Array<tvm::Type, void> const&, int, tvm::Attrs const&, tvm::TypeReporter const&)
  File "/root/anaconda3/conda-bld/tvm-package_1701590675822/work/src/relay/op/nn/pad.cc", line 131
InternalError: Check failed: (data->shape.size() == param->pad_width.size()) is false: There should be as many pad width pairs as shape dimensions but the shape has 5 dimensions and there are 4 pad width pairs.

Environment

Operating System: Ubuntu 18
TVM:0.15
Torch: 2.1.1
ONNX: 1.15.0

Steps to reproduce

ONNX file is here: onnx.zip

Here is the script

from onnxsim import simplify
import tvm
from tvm import relay
import onnx


def compile_onnx(onnx_model, shape):
    mod_from_onnx, params_onnx = relay.frontend.from_onnx(onnx_model,
                                                          shape=shape)
    with tvm.transform.PassContext(opt_level=4):
        executor = relay.build_module.create_executor(
            'graph', mod_from_onnx, tvm.cpu(), 'llvm', params_onnx
        ).evaluate()


model = onnx.load('./model.onnx')

try:
    compile_onnx(model, {'v0_0': [], 'v6_0': [5, 5, 4, 2, 1]})
except Exception as e:
    print(f"onnx fail\n{e}")

model_simp, check = simplify(model)

onnx.save(model_simp, "./model_simp.onnx")

assert check, "Simplified ONNX model could not be validated"

try:
    compile_onnx(model_simp, {'v0_0': [], 'v6_0': [5, 5, 4, 2, 1]})
except Exception as e:
    print(f"onnx-simplify fail\n{e}")

Triage

• needs-triage

[xhmelon]

Could you provide the simplified model for debugging?

[shaoyuyoung]

sorry for the late response.
below is the simplified model
model-sim.zip
In fact, the simplified model seems correct but the original model can't pass the compilation.
BTW, the simplified tool I used is here: https://github.com/daquexian/onnx-simplifier

@xhmelon

[shaoyuyoung]

Hello, sorry to bother you again but I still feel confused about this bug. @xhmelon

Is there any new progress on this issue?

I know maybe you have no time to investigate this because of your busy schedule.