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Posted to commits@tvm.apache.org by "cbalint13 (via GitHub)" <gi...@apache.org> on 2025/08/14 10:27:14 UTC
[PR] [Fix][ONNX] No precision widening for numpy binary operations [tvm]
cbalint13 opened a new pull request, #18207:
URL: https://github.com/apache/tvm/pull/18207
This PR enforce ONNX constraint compliance for binary operations.
---
#### Changes
* Overides the behaviour of numpy precision widening, e.g. ```np.div(int64,in64)->float64```
* This **now complies** with the [onnx.Div](https://onnx.ai/onnx/operators/onnx__Div.html) output constraint: ```C: Result, has same element type as two inputs```.
* Use an more appropriate public helper from API for the numpy type conversion (now it works with onnx 0.20 too).
---
#### Failure example
After shape sampling and division in the graph (excerpt from a custom onnx11n model) ```np.div(5,2)``` results in a ```float64``` (as per numpy behaviour) and then the input parameter of ```Slice``` fails upon its ```int64``` constraint.
<img width="381" height="620" alt="onnx-div-widening-issue" src="https://github.com/user-attachments/assets/60fbd252-81f9-4ed7-842d-a2040f5923bd" />
```
tvm.error.InternalError: Check failed: (is_base_of_tuple_of_int64(sinfo)) is false:
Operator Op(relax.strided_slice) requires the end argument to be a tuple of int64 PrimValues.
However, in expression {...}, the end argument (R.prim_value(T.float64(2.5)),) has struct info
R.Tuple(R.Prim(value=T.float64(2.5)))
```
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Re: [PR] [Fix][ONNX] No precision widening for numpy binary operations [tvm]
Posted by "tlopex (via GitHub)" <gi...@apache.org>.
tlopex merged PR #18207:
URL: https://github.com/apache/tvm/pull/18207
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Re: [PR] [Fix][ONNX] No precision widening for numpy binary operations [tvm]
Posted by "cbalint13 (via GitHub)" <gi...@apache.org>.
cbalint13 commented on PR #18207:
URL: https://github.com/apache/tvm/pull/18207#issuecomment-3188922420
Cc @Hzfengsy
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