gemini-code-assist[bot] commented on code in PR #18673:
URL: https://github.com/apache/tvm/pull/18673#discussion_r2704026659
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python/tvm/relax/frontend/onnx/onnx_frontend.py:
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@@ -1675,9 +1691,14 @@ def _impl_v1(cls, bb, inputs, attr, params):
output = cls.numpy_op(*np_inputs) # pylint: disable=not-callable
return relax.const(output, output.dtype)
- # Expand inputs, stack them, then perform minimum over the new axis.
- inputs = [bb.normalize(relax.op.expand_dims(i, axis=0)) for i in
inputs]
- stacked_tensor = relax.op.concat(inputs, axis=0)
+ input_shapes = [inp.struct_info.shape for inp in inputs]
+ current_target_shape = input_shapes[0]
+ for next_shape in input_shapes[1:]:
+ current_target_shape =
compute_broadcast_shape(current_target_shape, next_shape)
Review Comment:
For conciseness, you could use `functools.reduce` to compute the target
broadcast shape, which would replace this loop with a single line.
For example:
```python
import functools
...
current_target_shape = functools.reduce(compute_broadcast_shape,
input_shapes)
```
This would require adding `import functools` at the top of the file.
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