w-tingting opened a new issue #7872:
URL: https://github.com/apache/tvm/issues/7872
Hi! I quantified a model with Pytorch, and I am stuck with a problem in the
process of transforming this model with TVM.The code is as follows:
```
inp = torch.rand(1, 3, 32, 32)
model = Net(args.batch_size, args.mc, args.kernel_type,
args.num_classes).to(device)
qmodel = quantization_model(model)
qmodel.eval()
qmodel.qconfig = torch.quantization.get_default_qconfig('fbgemm')
qmodel = torch.quantization.prepare(model, inplace=False)
# qmodel(inp)
qmodel = torch.quantization.convert(qmodel, inplace=False)
qmodel.load_state_dict(torch.load('CIFAR10_net_04-17-2021_17-10-42.pth'))
script_module = torch.jit.trace(qmodel, inp).eval()
input_name = "input" # the input name can be be arbitrary for PyTorch
frontend.
input_shapes = [(input_name, (1, 3, 32, 32))]
mod, params = relay.frontend.from_pytorch(script_module, input_shapes)
tvm_result, rt_mod = run_tvm_model(mod, params, input_name, inp,
target="gpu")
```
The problem may arise in the code ` mod, params =
relay.frontend.from_pytorch(script_module, input_shapes) `
The error is as follows:
```
The Relay type checker is unable to show the following types match.
In particular dimension 0 conflicts: 1200 does not match 400.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(400), float32]` does not match `Tensor[(1200),
float32]`
note: run with `TVM_BACKTRACE=1` environment variable to display a backtrace.
```
As it said,I run it again after adding the `TVM_BACKTRACE=1` and I get the
error:
```
The Relay type checker is unable to show the following types match.
In particular dimension 0 conflicts: 1200 does not match 400.
The Relay type checker is unable to show the following types match.
In particular `Tensor[(400), float32]` does not match `Tensor[(1200),
float32]`
Traceback (most recent call last):
File "tvm_transform.py", line 84, in <module>
mod, params = relay.frontend.from_pytorch(script_module, input_shapes)
File "/home/wtt/tvm/python/tvm/relay/frontend/pytorch.py", line 3287, in
from_pytorch
ret = converter.convert_operators(_get_operator_nodes(graph.nodes()),
outputs, ret_name)[0]
File "/home/wtt/tvm/python/tvm/relay/frontend/pytorch.py", line 2711, in
convert_operators
self.record_output_type(relay_out)
File "/home/wtt/tvm/python/tvm/relay/frontend/pytorch.py", line 222, in
record_output_type
self.infer_type_with_prelude(output)
File "/home/wtt/tvm/python/tvm/relay/frontend/pytorch.py", line 170, in
infer_type_with_prelude
body = self.infer_type(val, self.prelude.mod)
File "/home/wtt/tvm/python/tvm/relay/frontend/pytorch.py", line 163, in
infer_type
new_mod = transform.InferType()(new_mod)
File "/home/wtt/tvm/python/tvm/ir/transform.py", line 127, in __call__
return _ffi_transform_api.RunPass(self, mod)
File "/home/wtt/tvm/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in
__call__
raise get_last_ffi_error()
tvm.error.DiagnosticError: Traceback (most recent call last):
5: TVMFuncCall
4: std::_Function_handler<void (tvm::runtime::TVMArgs,
tvm::runtime::TVMRetValue*), tvm::runtime::TypedPackedFunc<tvm::IRModule
(tvm::transform::Pass,
tvm::IRModule)>::AssignTypedLambda<tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#10}>(tvm::transform::{lambda(tvm::transform::Pass,
tvm::IRModule)#10}, std::__cxx11::basic_string<char, std::char_traits<char>,
std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&,
tvm::runtime::TVMRetValue*)#1}>::_M_invoke(std::_Any_data const&,
tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)
3: tvm::transform::Pass::operator()(tvm::IRModule) 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/wtt/tvm/src/ir/diagnostic.cc", line 105
DiagnosticError: one or more error diagnostics were emitted, please check
diagnostic render for output.
```
How can I solve this problem? Looking forward to your replay. Thank you!
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