Goose-Bomb opened a new issue, #13545:
URL: https://github.com/apache/tvm/issues/13545
### Actual behavior
When I try to apply `FakeQuantizationToInteger` pass on a Relay IRModule
imported from an ONNX model with QDQ quantization representation, it failed
with this error message:
```
......
at .../tvm/include/tvm/runtime/packed_func.h:1646
1: tvm::relay::qnn::QnnBroadcastRel(tvm::runtime::Array<tvm::Type, void>
const&, int, tvm::Attrs const&, tvm::TypeReporter const&)
at .../tvm/src/relay/qnn/op/op_common.h:310
0: tvm::relay::qnn::IsScalarType(tvm::Type const&, tvm::runtime::DataType
const&)
at .../tvm/src/relay/qnn/op/../utils.h:225
File ".../tvm/src/relay/analysis/type_solver.cc", line 624
TVMError:
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
Check failed: (false) is false: [16:42:40]
.../tvm/src/relay/qnn/op/../utils.h:225:
---------------------------------------------------------------
An error occurred during the execution of TVM.
For more information, please see: https://tvm.apache.org/docs/errors.html
---------------------------------------------------------------
Check failed: tensor_type->shape.size() == 0 (1 vs. 0) :
```
### Environment
TVM v0.10.0
### Steps to reproduce
Here is the code to reproduce the BUG:
```python3
model_onnx = onnx.load(onnx_path)
ir_mod, _ = relay.frontend.from_onnx(
model_onnx,
shape={"input": (1, 3, 224, 224)},
freeze_params=True
)
passes = tvm.transform.Sequential([
relay.transform.InferType(),
relay.transform.FakeQuantizationToInteger(),
])
ir_mod = passes(ir_mod)
```
Here is the quantized ONNX model with QDQ representation.
[regnet_x_400mf_qint8.onnx.tar.gz](https://github.com/apache/tvm/files/10146005/regnet_x_400mf_qint8.onnx.tar.gz)
### Triage
Please refer to the list of label tags
[here](https://github.com/apache/tvm/wiki/Issue-Triage-Labels) to find the
relevant tags and add them below in a bullet format (example below).
- relay:qnn
- frontend:onnx
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