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[Bug]: The inference result using CPU on MacOS M2 is abnormal, but the result using TEMPLATE device is normal #24481

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wanglxchina opened this issue May 13, 2024 · 10 comments
Open

[Bug]: The inference result using CPU on MacOS M2 is abnormal, but the result using TEMPLATE device is normal #24481

wanglxchina opened this issue May 13, 2024 · 10 comments
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@allnes @Munesh-Intel @Aznie-Intel
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category: CPU OpenVINO CPU plugin platform: arm OpenVINO on ARM / ARM64 support_request

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@wanglxchina
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OpenVINO Version

2024.0.0

Operating System

macOS Systems for Apple Silicon

Device used for inference

CPU

Framework

ONNX

Model used

No response

Issue description

Platform: Mac M2

  1. Using AUTO:CPU devices for inference, there were no errors during the process, calculations are fast, but the results were all 0
  2. Using AUTO devices for inference, calculations are slow, but the results are normal

Why is using the AUTO:CPU as an inference device causing abnormal results, while using AUTO is normal?

**use AUTO devices log:**
[17:03:29.2394]D[plugin.cpp:247][AUTO] deviceNameWithID:CPU, defaultDeviceID:, uniqueName:CPU_
[17:03:29.2394]D[plugin.cpp:247][AUTO] deviceNameWithID:TEMPLATE, defaultDeviceID:0, uniqueName:TEMPLATE_0
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:CPU, config:INFERENCE_NUM_THREADS=1
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:CPU, config:LOG_LEVEL=LOG_TRACE
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:CPU, config:PERF_COUNT=NO
[17:03:29.2408]I[plugin.cpp:423][AUTO] device:CPU, priority:0
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:TEMPLATE, config:LOG_LEVEL=LOG_TRACE
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:TEMPLATE, config:PERFORMANCE_HINT=LATENCY
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:TEMPLATE, config:PERFORMANCE_HINT_NUM_REQUESTS=0
[17:03:29.2408]I[plugin.cpp:421][AUTO] device:TEMPLATE, config:PERF_COUNT=NO
[17:03:29.2408]I[plugin.cpp:423][AUTO] device:TEMPLATE, priority:0
[17:03:29.2409]I[schedule.cpp:17][AUTO] scheduler starting
[17:03:29.2409]I[auto_schedule.cpp:131][AUTO] select device:TEMPLATE
[17:03:29.2409]I[auto_schedule.cpp:145][AUTO] will load CPU for accelerator
[17:03:29.2961]I[auto_schedule.cpp:109][AUTO] device:TEMPLATE compiling model finished
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:NETWORK_NAME=main_graph
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:SUPPORTED_PROPERTIES=NETWORK_NAME SUPPORTED_PROPERTIES EXECUTION_DEVICES LOADED_FROM_CACHE OPTIMAL_NUMBER_OF_INFER_REQUESTS DEVICE_ID PERF_COUNT
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:EXECUTION_DEVICES=TEMPLATE.0
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:LOADED_FROM_CACHE=NO
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:OPTIMAL_NUMBER_OF_INFER_REQUESTS=1
[17:03:29.2961]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:DEVICE_ID=0
[17:03:29.2962]D[auto_schedule.cpp:118][AUTO] device:TEMPLATE, GetConfig:PERF_COUNT=NO
[2024-05-13 17:03:29.297] [info] [main.cpp:13] model name: main_graph
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: ref_real
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: ref_imag
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: mic_real
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: mic_imag
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: in_state1
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,41,64]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     inputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input name: in_state2
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         input shape: [1,41,64]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     outputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output name: enhance_real
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output shape: [1,321,1]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     outputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output name: enhance_imag
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output shape: [1,321,1]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     outputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output name: out_state1
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output shape: [1,41,64]
[2024-05-13 17:03:29.297] [info] [main.cpp:13]     outputs
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output name: out_state2
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:03:29.297] [info] [main.cpp:13]         output shape: [1,41,64]
[2024-05-13 17:03:29.298] [info] [main.cpp:19] Step 2
[2024-05-13 17:03:29.298] [info] [main.cpp:19] Step 3
[2024-05-13 17:03:29.298] [info] [main.cpp:19] Step 4
[2024-05-13 17:03:29.300] [info] [main.cpp:19] Step 6
[2024-05-13 17:03:29.301] [info] [aec_ans_test.cpp:102]  start process ...
[17:03:29.3098]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
[17:03:29.3098]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:SUPPORTED_PROPERTIES=SUPPORTED_PROPERTIES NETWORK_NAME OPTIMAL_NUMBER_OF_INFER_REQUESTS NUM_STREAMS AFFINITY INFERENCE_NUM_THREADS PERF_COUNT INFERENCE_PRECISION_HINT PERFORMANCE_HINT EXECUTION_MODE_HINT PERFORMANCE_HINT_NUM_REQUEST
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:NETWORK_NAME=main_graph
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:OPTIMAL_NUMBER_OF_INFER_REQUESTS=1
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:NUM_STREAMS=1
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:AFFINITY=NONE
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:INFERENCE_NUM_THREADS=1
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERF_COUNT=NO
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:INFERENCE_PRECISION_HINT=f32
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERFORMANCE_HINT=LATENCY
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:EXECUTION_MODE_HINT=PERFORMANCE
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERFORMANCE_HINT_NUM_REQUESTS=0
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:ENABLE_CPU_PINNING=NO
[17:03:29.3109]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:SCHEDULING_CORE_TYPE=ANY_CORE
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:MODEL_DISTRIBUTION_POLICY=
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:ENABLE_HYPER_THREADING=NO
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:EXECUTION_DEVICES=CPU
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:CPU_DENORMALS_OPTIMIZATION=NO
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:LOG_LEVEL=LOG_TRACE
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE=1
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:DYNAMIC_QUANTIZATION_GROUP_SIZE=0
[17:03:29.3110]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:KV_CACHE_PRECISION=f16
[17:03:29.3110]I[auto_schedule.cpp:230][AUTO] release all work requests of CPU_HELP
[17:03:29.3122]I[auto_schedule.cpp:235][AUTO] helper released!!

**use AUTO:CPU as devices log:**

[17:08:58.7987]D[plugin.cpp:247][AUTO] deviceNameWithID:CPU, defaultDeviceID:, uniqueName:CPU_
[17:08:58.8007]I[plugin.cpp:421][AUTO] device:CPU, config:INFERENCE_NUM_THREADS=1
[17:08:58.8007]I[plugin.cpp:421][AUTO] device:CPU, config:LOG_LEVEL=LOG_TRACE
[17:08:58.8007]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT=LATENCY
[17:08:58.8007]I[plugin.cpp:421][AUTO] device:CPU, config:PERFORMANCE_HINT_NUM_REQUESTS=0
[17:08:58.8007]I[plugin.cpp:421][AUTO] device:CPU, config:PERF_COUNT=NO
[17:08:58.8007]I[plugin.cpp:423][AUTO] device:CPU, priority:0
[17:08:58.8007]I[schedule.cpp:17][AUTO] scheduler starting
[17:08:58.8007]I[auto_schedule.cpp:131][AUTO] select device:CPU
[17:08:58.8673]I[auto_schedule.cpp:109][AUTO] device:CPU compiling model finished
[17:08:58.8673]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:SUPPORTED_PROPERTIES=SUPPORTED_PROPERTIES NETWORK_NAME OPTIMAL_NUMBER_OF_INFER_REQUESTS NUM_STREAMS AFFINITY INFERENCE_NUM_THREADS PERF_COUNT INFERENCE_PRECISION_HINT PERFORMANCE_HINT EXECUTION_MODE_HINT PERFORMANCE_HINT_NUM_REQUEST
[17:08:58.8683]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:NETWORK_NAME=main_graph
[17:08:58.8683]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:OPTIMAL_NUMBER_OF_INFER_REQUESTS=1
[17:08:58.8683]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:NUM_STREAMS=1
[17:08:58.8683]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:AFFINITY=NONE
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:INFERENCE_NUM_THREADS=1
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERF_COUNT=NO
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:INFERENCE_PRECISION_HINT=f32
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERFORMANCE_HINT=LATENCY
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:EXECUTION_MODE_HINT=PERFORMANCE
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:PERFORMANCE_HINT_NUM_REQUESTS=0
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:ENABLE_CPU_PINNING=NO
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:SCHEDULING_CORE_TYPE=ANY_CORE
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:MODEL_DISTRIBUTION_POLICY=
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:ENABLE_HYPER_THREADING=NO
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:EXECUTION_DEVICES=CPU
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:CPU_DENORMALS_OPTIMIZATION=NO
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:LOG_LEVEL=LOG_TRACE
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE=1
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:DYNAMIC_QUANTIZATION_GROUP_SIZE=0
[17:08:58.8684]D[auto_schedule.cpp:118][AUTO] device:CPU, GetConfig:KV_CACHE_PRECISION=f16
[17:08:58.8684]I[plugin.cpp:451][AUTO] underlying hardware does not support hardware context
[2024-05-13 17:08:58.869] [info] [main.cpp:13] model name: main_graph
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: ref_real
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: ref_imag
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: mic_real
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: mic_imag
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,1,321]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: in_state1
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,41,64]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     inputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input name: in_state2
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         input shape: [1,41,64]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     outputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output name: enhance_real
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output shape: [1,321,1]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     outputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output name: enhance_imag
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output shape: [1,321,1]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     outputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output name: out_state1
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output shape: [1,41,64]
[2024-05-13 17:08:58.869] [info] [main.cpp:13]     outputs
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output name: out_state2
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output type: f32
[2024-05-13 17:08:58.869] [info] [main.cpp:13]         output shape: [1,41,64]```

### Step-by-step reproduction

_No response_

### Relevant log output

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### Issue submission checklist

- [X] I'm reporting an issue. It's not a question.
- [X] I checked the problem with the documentation, FAQ, open issues, Stack Overflow, etc., and have not found a solution.
- [X] There is reproducer code and related data files such as images, videos, models, etc.
@wanglxchina wanglxchina added bug Something isn't working support_request labels May 13, 2024
@avitial avitial added category: AUTO OpenVINO AUTO device selection plugin and removed bug Something isn't working labels May 13, 2024
@wanglxchina
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After testing, if we output the result of the last BN layer of the model, the final result is normal。

image

@Aznie-Intel
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Hi @wanglxchina , Do you still need help with this? If so, please let us know what model you use and share the link to your model if it's available publicly.

@wanglxchina
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Thank you for your reply. Unfortunately, the current model cannot be provided at the moment. This seems to be a bug in OpenVino. There was an error executing the BatchNorm2d operator on the arm64 platform. When exporting the BatchNorm2d execution result, the result was correct, but if not exported, the result is incorrect. There is no such issue on the X86_64 platform, everything is normal on the X86_64 platform;
Additionally, we will attempt a simple model with a similar structure for testing.
If more information is needed, I can provide it. Thank you.

@songbell
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@wanglxchina can you help a quick check of the result by using device directly as CPU (not AUTO:CPU)? what is the result on arm64 for CPU without reporting the BatchNorm2d execution result?

@wanglxchina
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@songbell Using CPU or AUTO:CPU results are both incorrect, and I have already tested them. The first thing I used was to directly use the CPU.If the execution results of BatchNorm2d are exported, both CPU and AUTO:CPU results are correct

@peterchen-intel peterchen-intel changed the title [Bug]: The inference result using CPU on MacOS M2 is abnormal, but the result using AUTO device is normal [Bug]: The inference result using CPU on MacOS M2 is abnormal, but the result using TEMPLATE device is normal May 15, 2024
@peterchen-intel peterchen-intel added category: CPU OpenVINO CPU plugin and removed category: AUTO OpenVINO AUTO device selection plugin labels May 15, 2024
@peterchen-intel
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@wanglxchina The difference is due to the TEMPLATE and CPU device. Will ask CPU engineer to take a look.

@dmitry-gorokhov
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@allnes Please take a look on the issue.

@wanglxchina We would appreciate if you can provide part of the model (or equivalent subgraph) that produces incorrect results. It will significantly simpify reproduction work on our side.

@wanglxchina
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wanglxchina commented May 15, 2024 •
edited

toy_model.zip

@dmitry-gorokhov this is a simple onnx model with a similar structure for testing. toy_model_multi_out.onnx export the execution results of BatchNorm2d and toy_model.onnx was not exported.

The results obtained by toy_model.onnx and toy_model_multi_out.onnx using CPU inference on the arm64 platform are different. But they should be the same.

The results obtained by toy_model.onnx using CPU inference on the arm64 platform is the same as the results obtained by toy_model.onnx using CPU inference on the x86_64 platform. This is in line with expectations.

@allnes
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allnes commented May 16, 2024

toy_model.zip

@dmitry-gorokhov this is a simple onnx model with a similar structure for testing. toy_model_multi_out.onnx export the execution results of BatchNorm2d and toy_model.onnx was not exported.

The results obtained by toy_model.onnx and toy_model_multi_out.onnx using CPU inference on the arm64 platform are different. But they should be the same.

The results obtained by toy_model.onnx using CPU inference on the arm64 platform is the same as the results obtained by toy_model.onnx using CPU inference on the x86_64 platform. This is in line with expectations.

Hi!
Thanks for models. I will return when I get some results about issue.

@dmitry-gorokhov dmitry-gorokhov added the platform: arm OpenVINO on ARM / ARM64 label May 17, 2024
@wanglxchina
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@allnes Hello, has there been any progress on this issue

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