tinywisdom opened a new issue, #18338:
URL: https://github.com/apache/tvm/issues/18338
### Expected behavior
+ `unbind` producing a single-element tuple should be represented as a tuple
in Relax IR, not misinterpreted as a tensor.
+ The frontend should either:
+ Correctly lower to Tuple with one element (each tensor shaped `(3,)` in
this case), or
+ Gracefully reject with a clear Python exception, not an internal
assertion failure.
### Actual behavior
When importing a PyTorch `torch.exported` program into TVM Relax, if the
model applies `unbind(dim=0)` to a tensor with dimension=1 along that axis, the
frontend crashes with:
```
Check failed: (opt) is false: The struct info of Tuple must be
TupleStructInfo,
but expression lv3 has struct info R.Tensor((1, 3), dtype="float32")
```
This indicates a mismatch between expected `TupleStructInfo` vs. actual
`TensorStructInfo`.
```
[INFO] start importing exported program into TVM Relax...
[REPRODUCED] Caught exception while importing:
Check failed: (opt) is false: The struct info of Tuple must be
TupleStructInfo,
but expression lv3 has struct info R.Tensor((1, 3), dtype="float32")
tvm.error.InternalError: Check failed: (opt) is false: The struct info of
Tuple must be TupleStructInfo, but expression lv3 has struct info R.Tensor((1,
3), dtype="float32")
[...]/src/relax/ir/block_builder.cc:64: Warning: BlockBuilder destroyed with
remaining blocks!
```
### Environment
+ OS: (Ubuntu 22.04.4 LTS (x86_64))
+ TVM version: (release v0.21.0)
+ Python: (3.10.16)
+ LLVM: (17.0.6)
### Steps to reproduce
```python
import torch
import torch.nn as nn
# Minimal model: gather -> max -> unbind(0)
class MyModel(nn.Module):
def forward(self, x, y):
# x: (2, 3), y: (1, 3)
indices = torch.zeros((1,) + x.size()[1:], dtype=torch.long,
device=x.device)
x_gathered = torch.gather(x, 0, indices) # (1, 3)
compared = torch.max(x_gathered, y) # (1, 3)
outs = compared.unbind(0) # tuple of length 1, each
(3,)
return outs
def get_inputs():
torch.manual_seed(0)
x = torch.randn(2, 3)
y = torch.randn(1, 3)
return x, y
if __name__ == "__main__":
from torch.export import export as torch_export
from tvm.relax.frontend.torch import from_exported_program
m = MyModel().eval()
args = get_inputs()
ep = torch_export(m, args)
print("[INFO] start importing exported program into TVM Relax...")
try:
mod = from_exported_program(ep)
print("[UNEXPECTED] Import succeeded (no error).")
except Exception as e:
print("[REPRODUCED] Caught exception while importing:")
print(e)
raise
```
### Triage
* needs-triage
* bug
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