[Bug] The Onnx Frontend incorrectly mapping the Operator implementation version

[locnd182644]

Description

The Onnx Frontend incorrectly mapping the Operator implementation version. Specifically, frontend has selected implement an operator with a higher version than the model's opset number.

Steps to Reproduce

• Graph: ReduceMean Node

• Using tvm.relax.frontend.onnx.from_onnx with ignore pass opset, Relax module wrong output shape.

Expected behavior

Mean Relax have axis = 2 and output shape = (1, 68, 18)

. . .

lv90: R.Tensor((1, 516, 4, 18), dtype="float32") = R.concat((lv86, lv87, lv88, lv89), axis=1)
lv91: R.Tensor((1, 68, 4, 18), dtype="float32") = R.nn.conv2d(lv90, metadata["relax.expr.Constant"][30], strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
lv92: R.Tensor((1, 68, 1, 1), dtype="float32") = R.reshape(metadata["relax.expr.Constant"][31], R.shape([1, 68, 1, 1]))
lv93: R.Tensor((1, 68, 4, 18), dtype="float32") = R.add(lv91, lv92)
gv: R.Tensor((1, 68, 18), dtype="float32") = R.mean(lv93, axis=[2], keepdims=False)
R.output(gv)

Actual behavior

Mean Relax have axis = None and output shape = ( )

. . .

lv90: R.Tensor((1, 516, 4, 18), dtype="float32") = R.concat((lv86, lv87, lv88, lv89), axis=1)
lv91: R.Tensor((1, 68, 4, 18), dtype="float32") = R.nn.conv2d(lv90, metadata["relax.expr.Constant"][30], strides=[1, 1], padding=[0, 0, 0, 0], dilation=[1, 1], groups=1, data_layout="NCHW", kernel_layout="OIHW", out_layout="NCHW", out_dtype="void")
lv92: R.Tensor((1, 68, 1, 1), dtype="float32") = R.reshape(metadata["relax.expr.Constant"][31], R.shape([1, 68, 1, 1]))
lv93: R.Tensor((1, 68, 4, 18), dtype="float32") = R.add(lv91, lv92)
gv: R.Tensor((), dtype="float32") = R.mean(lv93, axis=None, keepdims=False)
R.output(gv)

Debug

• In method get_converter of OnnxOpConverter, the impl_v mapping algorithm takes the highest version among all versions <= opset. If there is no implementation version <= opset, the index of the versions will be a negative number.
• Rotate to take the latest Implement of Op (Negative indexing starts at -1 for the last element) -> Wrong Implement Operator

Go back example: ReduceMean Operator in Onnx

• Currently, in tvm, ReduceMean have 2 implements ( _impl_v13 and _impl_v18)
• Opset model = 9 -> get _impl_v18. Right must get _impl_v1
• In ReduceMean, version 1 and version 18 have different about Inputs and Attributes. (Ref: https://onnx.ai/onnx/operators/text_diff_ReduceMean_1_18.html)

Possible fix method

• Suggestion 1: Using onnx.defs.get_schema
  • Using "from onnx import defs" to look up the Operator schema based on the model's opset. Then get Operator Version Exactly to Implement. Result as ai.onnx.v1 in Image.
  • Todo: Implement all Version of ONNX Operator.

• Suggestion 2: Add check empty impl, raise error
  • In method get_converter of OnnxOpConverter, filter Impl Version <= Opset, then get max this list versions to take Impl Version of Op. If this list filter versions are Empty, raise "Not found implement of operator compatible with the model's opset".
  • Todo: Implement version Op with different schemas (input, atrr)

Triage

• needs-triage
• type: bug
• frontend:onnx

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What do you think about this issue? I would be happy to hear different opinions from everyone.

Thank you!