adityasingh2400 commented on PR #20092:
URL: https://github.com/apache/tvm/pull/20092#issuecomment-5196452587
You are right, and thanks for catching it. Fixed in dd1477d.
I confirmed the collapse you describe. `get_info` starts from `shape = []`
and only appends per dim, so it never returns `None`, and `_parse_graph_input`
feeds that straight into `_new_var`. Both an absent shape field and a
present-but-empty one therefore became `R.Tensor(())`, which means my `is None`
check was unreachable for an unknown-rank input and such an input would have
folded to `R.shape([])`.
`get_info` now returns `None` for the shape when
`tensor_type.HasField("shape")` is false. That matches what `TensorType`
already documents, `shape: Optional[Expr]` with `ndim` defaulting to -1 for the
unknown-rank form, so `_new_var(shape=None)` produces a tensor with no static
shape and the `is None` branch in `Shape` becomes reachable and correct. The
call site now also warns on unknown rank, matching the existing warning for
unknown dimensions.
I checked the premise against real protos rather than assuming:
| input | `HasField("shape")` | dims |
|---|---|---|
| `make_tensor_value_info(..., [])` rank-0 | True | 0 |
| `make_tensor_value_info(..., None)` unknown rank | False | 0 |
| `make_tensor_value_info(..., [2, 3])` | True | 2 |
Added `test_shape_unknown_rank_input`, which asserts the input keeps `shape
is None` and `ndim == -1` rather than becoming rank-0, and that `Shape` still
emits `relax.shape_of`. It also asserts the absent shape field up front so the
test cannot silently stop covering the case.
One caveat on verification: I could not run the ONNX frontend tests locally,
since that needs a built TVM. The proto behaviour above is measured, the rest
is reasoned from `get_info`'s single call site and the `TensorType` contract,
so CI is the real check here. Happy to iterate if it comes back unhappy.
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