🐛 Bug
timm_efficientdet inference fails to run with both dynamo and non-dynamo configurations. See the error below:
Traceback (most recent call last):
File "xla/benchmarks/experiment_runner.py", line 945, in <module>
main()
File "xla/benchmarks/experiment_runner.py", line 941, in main
runner.run()
File "xla/benchmarks/experiment_runner.py", line 61, in run
self.run_single_config()
File "xla/benchmarks/experiment_runner.py", line 256, in run_single_config
metrics, last_output = self.run_once_and_gather_metrics(
File "xla/benchmarks/experiment_runner.py", line 345, in run_once_and_gather_metrics
output, _ = loop(iter_fn=self._default_iter_fn)
File "xla/benchmarks/experiment_runner.py", line 302, in loop
output, timing, trace = iter_fn(benchmark_experiment, benchmark_model,
File "xla/benchmarks/experiment_runner.py", line 218, in _default_iter_fn
output = benchmark_model.model_iter_fn(
File "xla/benchmarks/benchmark_model.py", line 170, in eval
pred = self.module(*inputs)
File "torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/lib/python3.8/site-packages/effdet/bench.py", line 110, in forward
return _batch_detection(
RuntimeError: The following operation failed in the TorchScript interpreter.
Traceback of TorchScript (most recent call last):
RuntimeError: torch_xla/csrc/tensor_impl.cpp:138 : Check failed: !has_symbolic_sizes_strides_
*** Begin stack trace ***
tsl::CurrentStackTrace[abi:cxx11]()
torch_xla::XLATensorImpl::sizes_custom() const
at::FunctionalTensorWrapper::sizes_custom() const
c10::TensorType::create(at::Tensor const&)
torch::jit::tensorTypeInCurrentExecutionContext(at::Tensor const&)
_PyObject_MakeTpCall
PyVectorcall_Call
_PyObject_MakeTpCall
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
PyVectorcall_Call
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
PyVectorcall_Call
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
_PyObject_FastCallDict
_PyObject_Call_Prepend
PyObject_Call
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
_PyEval_EvalFrameDefault
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
_PyFunction_Vectorcall
_PyEval_EvalFrameDefault
_PyEval_EvalFrameDefault
_PyEval_EvalFrameDefault
_PyEval_EvalFrameDefault
_PyEval_EvalCodeWithName
PyEval_EvalCodeEx
PyEval_EvalCode
PyRun_SimpleFileExFlags
Py_RunMain
Py_BytesMain
__libc_start_main
_start
*** End stack trace ***
Cannot call sizes_custom() on an XLA tensor with symbolic sizes/strides
Affected Configurations
- Inference+Dynamo
- Inference+NonDynamo
Environment
- Reproducible on XLA backend [CPU/TPU]: CUDA
- torch_xla version: 5c48be1
cc @miladm @JackCaoG @vanbasten23 @cota @golechwierowicz @frgossen @zpcore
🐛 Bug
timm_efficientdetinference fails to run with both dynamo and non-dynamo configurations. See the error below:Affected Configurations
Environment
cc @miladm @JackCaoG @vanbasten23 @cota @golechwierowicz @frgossen @zpcore