fengzi0 opened a new issue, #15155:
URL: https://github.com/apache/tvm/issues/15155
When i load Pytorch fx quantized model to TVM like below code:
```
import torch
from torch.ao.quantization import get_default_qconfig_mapping,
get_default_qat_qconfig_mapping, quantize_fx
import tvm
from tvm import relay
qconfig_mapping = get_default_qconfig_mapping()
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(256, 256)
def forward(self, input):
x = self.linear(input)
return x
mm = MyModule()
input = torch.randn((1,900,256))
mm_prepared = quantize_fx.prepare_fx(mm, qconfig_mapping, (input))
r = mm_prepared(input)
mm_quantize = quantize_fx.convert_fx(mm_prepared)
script_mm = torch.jit.trace(mm_quantize, (input))
input_shapes_mm = [('input', tuple(input.shape))]
mod, params = relay.frontend.from_pytorch(script_mm, input_shapes_mm)
```
this give:
```
The Relay type checker is unable to show the following types match:
Tensor[(900), int32]
Tensor[(256), int32]
In particular:
dimension 0 conflicts: 900 does not match 256.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(256), int32]` does not match `Tensor[(900), int32]`
The Relay type checker is unable to show the following types match:
Tensor[(900), float32]
Tensor[(256), float32]
In particular:
dimension 0 conflicts: 900 does not match 256.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(256), float32]` does not match `Tensor[(900),
float32]`
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/relay/frontend/pytorch.py",
line 4649, in from_pytorch
outputs =
converter.convert_operators(_get_operator_nodes(graph.nodes()), outputs,
ret_name)
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/relay/frontend/pytorch.py",
line 4025, in convert_operators
self.record_output_type(relay_out)
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/relay/frontend/pytorch.py",
line 220, in record_output_type
self.infer_type_with_prelude(output)
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/relay/frontend/pytorch.py",
line 168, in infer_type_with_prelude
body = self.infer_type(val, self.prelude.mod)
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/relay/frontend/pytorch.py",
line 161, in infer_type
new_mod = transform.InferType()(new_mod)
File
"/opt/conda/envs/python3_9/lib/python3.9/site-packages/tvm/ir/transform.py",
line 161, in __call__
return _ffi_transform_api.RunPass(self, mod)
File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in
tvm._ffi._cy3.core.PackedFuncBase.__call__
File "tvm/_ffi/_cython/./packed_func.pxi", line 262, in
tvm._ffi._cy3.core.FuncCall
File "tvm/_ffi/_cython/./packed_func.pxi", line 251, in
tvm._ffi._cy3.core.FuncCall3
File "tvm/_ffi/_cython/./base.pxi", line 181, in
tvm._ffi._cy3.core.CHECK_CALL
tvm.error.DiagnosticError: Traceback (most recent call last):
6: TVMFuncCall
5:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::transform::Pass,
tvm::IRModule)>::AssignTypedLambda<tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}>(tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#7}, std::string)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
4: tvm::transform::Pass::operator()(tvm::IRModule) 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:
tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<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}> >::Call(tvm::runtime::PackedFuncObj const*,
tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
0: tvm::DiagnosticContext::Render()
File "/workspace/tvm/src/ir/diagnostic.cc", line 105
DiagnosticError: one or more error diagnostics were emitted, please check
diagnostic render for output.
```
version info:
```
>>> torch.__version__
'2.0.0+cu117'
>>> tvm.__version__
'0.11.1'
```
Wondering is there something obvious that I should fix? Thanks!
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