aryanputta opened a new pull request, #20119:
URL: https://github.com/apache/tvm/pull/20119
Fixes #20117.
## Problem
`MultiInputBase._impl_v1` folds all-constant operands with:
```python
output = cls.numpy_op(*np_inputs)
```
`numpy_op` is a reduction (`np.min`, `np.max`, `np.sum`, `np.mean`), whose
signature is `op(a, axis=None, ...)`. Passing the operands positionally binds
the second constant to `axis` instead of combining it with the first.
Importing a model where `Min`, `Max`, `Sum` or `Mean` has only constant
inputs therefore raises:
```
TypeError: only integer scalar arrays can be converted to a scalar index
```
There is a quieter case. When the second operand is a rank-0 integer that is
a valid axis, numpy accepts it and nothing raises. The fold returns a reduction
of the first operand, with the wrong shape and the wrong values:
```python
a = np.arange(1, 7).reshape(3, 2) # the first constant
b = np.array(0) # a rank-0 constant, a valid axis
np.min(a, b) # -> [1, 2] shape (2,)
# elementwise min ->
[[0,0],[0,0],[0,0]] shape (3, 2)
```
The issue reports `Min` with two rank-1 constants, but the defect is in the
shared base class, so `Max`, `Sum` and `Mean` are affected identically.
## Fix
Broadcast the operands, stack them on a new leading axis, then reduce over
it. That is exactly what the non-constant path immediately below already builds
with `broadcast_to`, `stack` and `relax_op`, so both paths now compute one
definition.
## Tests
`test_multi_input_all_constant_inputs` covers all four operators through
`check_correctness`, which compares the imported module against onnxruntime.
`Sum` and `Mean` accept only floating point operands in ONNX, so the integer
cases use `Min` and `Max`. Those cases pass a rank-0 operand holding a valid
axis index (`0` and `1`), since an out-of-range value would raise and would not
reach the silent path.
Every case fails before this change and passes after: the four float cases
raise `TypeError`, and the two integer cases return a wrong result.
## Verification
Across 444 combinations of the four operators, six shapes including rank-0
and broadcasting pairs, three dtypes, and operand counts of one, three and
four, the new fold agrees with the broadcast + stack + reduce path in every
case. Over the same set the old fold raised in 425 and returned a wrong answer
in 7.
The six cases added here were each checked against onnxruntime directly, and
the folded values match its output.
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