Onnxruntime-directml 1.18.0 broken multithreading inference session

[Djdefrag]

Describe the issue

With the new version 1.18 it seems that trying to use different InferenceSession using the same DirectML device, all threads remain stalled without giving any exception or error

To reproduce

Thread 1

 AI_model_loaded = onnx_load(AI_model_path)

AI_model = onnxruntime_inferenceSession(
    path_or_bytes = AI_model_loaded.SerializeToString(), 
    providers =  [('DmlExecutionProvider', {"device_id": "0"})]
)    

onnx_input  = {AI_model.get_inputs()[0].name: image}
onnx_output = AI_model.run(None, onnx_input)[0]

Thread n (where n can be any number)

AI_model_loaded = onnx_load(AI_model_path)

AI_model = onnxruntime_inferenceSession(
    path_or_bytes = AI_model_loaded.SerializeToString(), 
    providers =  [('DmlExecutionProvider', {"device_id": "0"})]
)    

onnx_input  = {AI_model.get_inputs()[0].name: image}
onnx_output = AI_model.run(None, onnx_input)[0]

Urgency

No response

Platform

Windows

OS Version

10

ONNX Runtime Installation

Released Package

ONNX Runtime Version or Commit ID

1.18.0

ONNX Runtime API

Python

Architecture

X64

Execution Provider

DirectML

Execution Provider Library Version

1.18.0

[sophies927]

Tagging @PatriceVignola @smk2007 @fdwr for visibility.

[saulthu]

Same here on Windows, versions 1.16.0 to 1.17.3 work fine over multiple threads, however 1.18.0 gives Windows fatal exception: access violation with the following stack trace produced by my own Windows SEH handler:

-----------
Caught unhandled exception...
-----------

Terminating from thread id 10152

Non-C++ exception:
  Error: EXCEPTION_ACCESS_VIOLATION
  Type: Read
  Addr: 0x0

Trace:
 40:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 39:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 38:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 37:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 36:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 35:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 34:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 33:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 32:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 31:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 30:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 29:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 28:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 27:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 26:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 25:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 24:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 23:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 22:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 21:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 20:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 19:  ?: PyInit_onnxruntime_pybind11_state  (onnxruntime_pybind11_state.pyd)
 18:  ?: pybind11::error_already_set::discard_as_unraisable  (onnxruntime_pybind11_state.pyd)
 17:  ?: PyObject_MakeTpCall  (python311.dll)
 16:  ?: PyObject_Vectorcall  (python311.dll)
 15:  ?: PyEval_EvalFrameDefault  (python311.dll)
 14:  ?: PyFunction_Vectorcall  (python311.dll)
 13:  ?: PyFunction_Vectorcall  (python311.dll)
 12:  ?: PyObject_CallObject  (python311.dll)
 11:  ?: PyEval_EvalFrameDefault  (python311.dll)
 10:  ?: PyFunction_Vectorcall  (python311.dll)
  9:  ?: PyObject_CallObject  (python311.dll)
  8:  ?: PyEval_EvalFrameDefault  (python311.dll)
  7:  ?: PyFunction_Vectorcall  (python311.dll)
  6:  ?: PyFunction_Vectorcall  (python311.dll)
  5:  ?: PyObject_Call  (python311.dll)
  4:  ?: PyInterpreterState_Delete  (python311.dll)
  3:  ?: PyInterpreterState_Delete  (python311.dll)
  2:  ?: recalloc  (ucrtbase.dll)
  1:  ?: BaseThreadInitThunk  (KERNEL32.DLL)
  0:  ?: RtlUserThreadStart  (ntdll.dll)

[liuyunms]

We’ve noted the issue with GPU resource contention due to multiple threads. This usage pattern is not recommended as it makes multiple threads request all of the GPU resources, and can cause contention. Also, the allocator in python API (both CUDA and DML) is explicitly not thread safe because it initializes the allocator as a global singleton due it living outside of the session.

We’re investigating the recent failure and will address it. Meanwhile, please avoid this pattern to prevent GPU contention.

[Djdefrag]

Hi @liuyunms

Sorry to bother, I'm currently using an InferenceSession per tread, but you say it shouldn't be used this way.

4 threds -> 4 inference session with same gpu

Do you mean to use the same InferenceSession in multiple threads? Is it possible?

4 threds -> 1 inference session with same gpu

[Djdefrag]

@PatriceVignola @smk2007 @fdwr

Hi, sorry to bother, there are some news for this problem? Actually testing 1.18.1 and the problem is still present :(

Thank you