jikechao opened a new issue, #15282:
URL: https://github.com/apache/tvm/issues/15282
torch.nn.functional.instance_norm(args[0], use_input_stats=True) and
input_dtype=float64 wil lead to a crash:
**Error: tensor type `Tensor[(1), float64]` has 1 dimensions, while
`float64` has 0 dimension**
### Actual behavior
```
Traceback (most recent call last):
File "test.py", line 20, in <module>
mod, params = relay.frontend.from_pytorch(trace, input_shapes)
File "/workplace/software/tvm/tvm_/python/tvm/relay/frontend/pytorch.py",
line 5002, in from_pytorch
outputs = converter.convert_operators(operator_nodes, outputs, ret_name)
File "/workplace/software/tvm/tvm_/python/tvm/relay/frontend/pytorch.py",
line 4263, in convert_operators
self.record_output_type(relay_out)
File "/workplace/software/tvm/tvm_/python/tvm/relay/frontend/pytorch.py",
line 238, in record_output_type
self.infer_type_with_prelude(output)
File "/workplace/software/tvm/tvm_/python/tvm/relay/frontend/pytorch.py",
line 174, in infer_type_with_prelude
body = self.infer_type(val, self.prelude.mod)
File "/workplace/software/tvm/tvm_/python/tvm/relay/frontend/pytorch.py",
line 167, in infer_type
new_mod = transform.InferType()(new_mod)
File "/workplace/software/tvm/tvm_/python/tvm/ir/transform.py", line 160,
in __call__
return _ffi_transform_api.RunPass(self, mod)
File
"/workplace/software/tvm/tvm_/python/tvm/_ffi/_ctypes/packed_func.py", line
237, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
10: TVMFuncCall
9:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::transform::Pass,
tvm::IRModule)>::AssignTypedLambda<tvm::transform::$_6>(tvm::transform::$_6,
std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char>
>)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}>
>::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*)
8: tvm::transform::Pass::operator()(tvm::IRModule) const
7: tvm::transform::Pass::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
6: tvm::transform::ModulePassNode::operator()(tvm::IRModule,
tvm::transform::PassContext const&) const
5:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::IRModule,
tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::InferType()::$_2>(tvm::relay::transform::InferType()::$_2)::{lambda(tvm::runtime::TVMArgs
const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj
const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
4: tvm::DiagnosticContext::Render()
3: tvm::DiagnosticRenderer::Render(tvm::DiagnosticContext const&)
2:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<void
(tvm::DiagnosticContext)>::AssignTypedLambda<tvm::TerminalRenderer(std::ostream&)::$_10>(tvm::TerminalRenderer(std::ostream&)::$_10)::{lambda(tvm::runtime::TVMArgs
const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj
const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
1: tvm::ReportAt(tvm::DiagnosticContext const&, std::ostream&, tvm::Span
const&, tvm::Diagnostic const&)
0: _ZN3tvm7runtime6detail
File "/workplace/software/tvm/tvm_/src/ir/diagnostic.cc", line 264
TVMError: The source maps are not populated for this module. Please use
`tvm.relay.transform.AnnotateSpans` to attach source maps for error reporting.
Error: tensor type `Tensor[(1), float64]` has 1 dimensions, while `float64`
has 0 dimensions
```
### Steps to reproduce
```
import torch
from tvm import relay
import tvm
import numpy as np
from torch.nn import Module
input_data = torch.randn([1, 1, 1, 2], dtype=torch.float64)
class instance_norm(Module):
def forward(self, *args):
return torch.nn.functional.instance_norm(args[0],
use_input_stats=True)
m = instance_norm().float().eval()
torch_outputs = m(input_data)
trace = torch.jit.trace(m, input_data)
input_shapes = [('input0', torch.Size([1, 1, 1, 2]))]
mod, params = relay.frontend.from_pytorch(trace, input_shapes)
```
### Triage
* needs-triage
* frontend:pytorch
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