mkroening opened a new issue #9656:
URL: https://github.com/apache/tvm/issues/9656
### Expected behavior
No issue when running
```console
tvmc tune --target llvm --output autotuner_records.json model.onnx
```
with the ONNX model only being a multiplication of two four by four matrices
(three by three works).
Note that compiling the model without tuning works and produces valid
results.
Model:
```onnx
ir_version: 8
graph {
node {
input: "A"
input: "B"
output: "Y"
op_type: "Gemm"
}
name: "test-model"
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 4
}
dim {
dim_value: 4
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 4
}
dim {
dim_value: 4
}
}
}
}
}
output {
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 4
}
dim {
dim_value: 4
}
}
}
}
}
}
opset_import {
version: 15
}
```
### Actual behavior
```
# [..]
Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/__main__.py",
line 24, in <module>
tvmc.main.main()
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/main.py",
line 100, in main
sys.exit(_main(sys.argv[1:]))
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/main.py",
line 93, in _main
return args.func(args)
File
"/home/mkroening/Development/tvm/python/tvm/driver/tvmc/autotuner.py", line
266, in drive_tune
tune_model(
File
"/home/mkroening/Development/tvm/python/tvm/driver/tvmc/autotuner.py", line
486, in tune_model
tune_tasks(tasks, tuning_records, **tuning_options)
File
"/home/mkroening/Development/tvm/python/tvm/driver/tvmc/autotuner.py", line
687, in tune_tasks
tuner_obj.tune(
File
"/home/mkroening/Development/tvm/python/tvm/autotvm/tuner/xgboost_tuner.py",
line 105, in tune
super(XGBTuner, self).tune(*args, **kwargs)
File "/home/mkroening/Development/tvm/python/tvm/autotvm/tuner/tuner.py",
line 169, in tune
self.update(inputs, results)
File
"/home/mkroening/Development/tvm/python/tvm/autotvm/tuner/model_based_tuner.py",
line 281, in update
self.cost_model.fit(self.xs, self.ys, self.plan_size)
File
"/home/mkroening/Development/tvm/python/tvm/autotvm/tuner/xgboost_cost_model.py",
line 179, in fit
x_train = self._get_feature(xs)
File
"/home/mkroening/Development/tvm/python/tvm/autotvm/tuner/xgboost_cost_model.py",
line 341, in _get_feature
ret[i, :] = t if t is not None else 0
ValueError: could not broadcast input array from shape (468,) into shape
(426,)
Done.
```
### Environment
Operating System: Ubuntu 20.04.3 LTS
TVM version: ccd59e89d21cc81cc06f2a16cddcc1ffeed1e2a1
### Steps to reproduce
Create `model.onnx` with:
```python
import onnx
from onnx import helper
from onnx import TensorProto
l = 4
m = 4
n = 4
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [l, m])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [m, n])
Y = helper.make_tensor_value_info('Y', TensorProto.FLOAT, [l, n])
node_def = helper.make_node(
'Gemm', # name
['A', 'B'], # inputs
['Y'], # outputs
)
graph_def = helper.make_graph(
[node_def], # nodes
'test-model', # name
[A, B], # inputs
[Y], # outputs
)
model_def = helper.make_model(graph_def)
print('The model is:\n{}'.format(model_def))
onnx.checker.check_model(model_def)
print('The model is checked!')
onnx.save(model_def, 'model.onnx')
```
Thanks a lot for your help! :)
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