mkroening opened a new issue #9885:
URL: https://github.com/apache/tvm/issues/9885
### Expected behavior
No issue when running
```console
tvmc compile --target llvm --output out.tar model.onnx
```
with the ONNX model containing a `ConvTranspose` with `group != 1` (ONNX
Runtime is working fine).
Model:
```onnx
ir_version: 8
graph {
node {
input: "A"
input: "B"
output: "C"
op_type: "ConvTranspose"
attribute {
name: "group"
i: 2
type: INT
}
}
name: "test-model"
input {
name: "A"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
input {
name: "B"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
output {
name: "C"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 2
}
dim {
dim_value: 1
}
dim {
dim_value: 1
}
}
}
}
}
}
opset_import {
version: 15
}
```
### Actual behavior
```
# [..]
The Relay type checker is unable to show the following types match.
In particular dimension 1 conflicts: 0 does not match 1.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(2, 1, 1, 1), float32]` does not match `Tensor[(2, 0,
1, 1), float32]`
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 94, in main
sys.exit(_main(sys.argv[1:]))
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/main.py",
line 87, in _main
return args.func(args)
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/compiler.py",
line 141, in drive_compile
compile_model(
File "/home/mkroening/Development/tvm/python/tvm/driver/tvmc/compiler.py",
line 271, in compile_model
graph_module = relay.build(mod, target=tvm_target, params=params)
File "/home/mkroening/Development/tvm/python/tvm/relay/build_module.py",
line 369, in build
executor_config, runtime_mod, params = bld_mod.build(
File "/home/mkroening/Development/tvm/python/tvm/relay/build_module.py",
line 177, in build
self._build(mod, target, target_host, executor, mod_name)
File "tvm/_ffi/_cython/./packed_func.pxi", line 323, in
tvm._ffi._cy3.core.PackedFuncBase.__call__
File "tvm/_ffi/_cython/./packed_func.pxi", line 267, in
tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./base.pxi", line 163, in tvm._ffi._cy3.core.CALL
tvm.error.DiagnosticError: Traceback (most recent call last):
12: TVMFuncCall
11: std::_Function_handler<void (tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*),
tvm::relay::backend::RelayBuildModule::GetFunction(std::string const&,
tvm::runtime::ObjectPtr<tvm::runtime::Object>
const&)::{lambda(tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)#3}>::_M_invoke(std::_Any_data const&,
tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
10: tvm::relay::backend::RelayBuildModule::BuildRelay(tvm::IRModule,
std::unordered_map<std::string, tvm::runtime::NDArray, std::hash<std::string>,
std::equal_to<std::string>, std::allocator<std::pair<std::string const,
tvm::runtime::NDArray> > > const&, tvm::runtime::String)
9: tvm::relay::backend::RelayBuildModule::OptimizeImpl(tvm::IRModule,
std::unordered_map<std::string, tvm::runtime::NDArray, std::hash<std::string>,
std::equal_to<std::string>, std::allocator<std::pair<std::string const,
tvm::runtime::NDArray> > > const&)
8: tvm::transform::Pass::operator()(tvm::IRModule) const
7: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
6: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
5: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
4: tvm::transform::SequentialNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
3: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
2: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
1: std::_Function_handler<void (tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::{lambda(tvm::IRModule,
tvm::transform::PassContext
const&)#1}>(tvm::relay::transform::InferType()::{lambda(tvm::IRModule,
tvm::transform::PassContext const&)#1})::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&,
tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
0: tvm::DiagnosticContext::Render()
File "/home/mkroening/Development/tvm/src/ir/diagnostic.cc", line 105
DiagnosticError: one or more error diagnostics were emitted, please check
diagnostic render for output.
```
### Environment
Operating System: Ubuntu 20.04.3 LTS
TVM version: 4babd36481b7108bf50df5c3b256c95c0d9c3291
### Steps to reproduce
Create `model.onnx` with:
```python
import onnx
from onnx import helper
from onnx import TensorProto
n = 1
c_in = 2
h_in = 1
w_in = 1
c_out = 2
h_out = 1
w_out = 1
groups = 2
kernel_size = [1, 1]
A = helper.make_tensor_value_info(
'A', TensorProto.FLOAT, [n, c_in, h_in, w_in])
B = helper.make_tensor_value_info(
'B', TensorProto.FLOAT, [c_out, int(c_in / groups), kernel_size[0],
kernel_size[1]])
C = helper.make_tensor_value_info(
'C', TensorProto.FLOAT, [n, c_out, h_out, w_out])
node_def = helper.make_node(
'ConvTranspose', # name
['A', 'B'], # inputs
['C'], # outputs
group=groups,
)
graph_def = helper.make_graph(
[node_def], # nodes
'test-model', # name
[A, B], # inputs
[C], # 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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