napronald opened a new pull request, #20050:
URL: https://github.com/apache/tvm/pull/20050
## Summary
This adds support for the `start` and `end` attributes introduced for ONNX
`Shape` in opset 15.
The Relax ONNX frontend previously reused the opset 13 implementation, which
always returned the full input shape. As a result, models using sliced shape
values could construct an incorrect target shape and fail in downstream
operators such as `Reshape`:
```text
ValueError: Reshape expects the new shape to be convertible from the old
shape. However, the old shape is R.shape([12]), with product T.int64(12), while
the new shape is R.shape([2, 3, 4]), with product T.int64(24)
```
### Minimal reproduce
```python
import onnx
from tvm.relax.frontend.onnx import from_onnx
input_shape = [2, 3, 4]
data_shape = [12]
expected_shape = [3, 4]
start = 1
end = None
opset = 15
shape_attrs = {"start": start}
if end is not None:
shape_attrs["end"] = end
model = onnx.helper.make_model(
onnx.helper.make_graph(
[
onnx.helper.make_node("Shape", ["x"], ["shape"], **shape_attrs),
onnx.helper.make_node("Reshape", ["data", "shape"], ["y"]),
],
"shape_start_end_repro",
[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, input_shape
),
onnx.helper.make_tensor_value_info(
"data", onnx.TensorProto.FLOAT, data_shape
),
],
[
onnx.helper.make_tensor_value_info(
"y", onnx.TensorProto.FLOAT, expected_shape
)
],
),
opset_imports=[onnx.helper.make_opsetid("", opset)],
)
print(f"Shape attributes: start={start}, end={end}")
print(f"Expected Shape output: {input_shape[start:end]}")
print(from_onnx(model, opset=opset).script())
```
The new implementation applies `start` and `end` slicing to static and
symbolic shape expressions. It also handles runtime-produced shape values by
converting them to a tensor, applying `strided_slice`, and converting the
result back to a shape.
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