Incorrect number of network outputs detected during int8 calibration leading to failure #3339

BrettRyland · 2023-09-20T16:02:56Z

Description

When trying to convert a PyTorch model to a TensorRT engine, int8 calibration fails with:

brett@brett-home:~/Work/Autosensor/NN$ python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x512x896-complexity:0-statnett-0.5-2023-08-01" -q
/home/brett/Work/Autosensor/.direnv/python-venv-3.11.5/lib/python3.11/site-packages/torch/__init__.py:1418: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/home/brett/Work/Autosensor/.direnv/python-venv-3.11.5/lib/python3.11/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /tmp/build-via-sdist-9wtz2njt/torch-2.2.0a0+gitfaf3de3/aten/src/ATen/native/TensorShape.cpp:3549.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[09/20/2023-16:57:36] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 541, GPU 4118 (MiB)
[09/20/2023-16:57:39] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1444, GPU +266, now: CPU 2062, GPU 4384 (MiB)
[09/20/2023-16:57:39] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/20/2023-16:57:39] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[09/20/2023-16:57:39] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[09/20/2023-16:57:39] [TRT] [I] Graph optimization time: 0.00566967 seconds.
[09/20/2023-16:57:39] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2085, GPU 4392 (MiB)
[09/20/2023-16:57:39] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2086, GPU 4402 (MiB)
[09/20/2023-16:57:39] [TRT] [I] Timing cache disabled. Turning it on will improve builder speed.
[09/20/2023-16:57:42] [TRT] [I] Detected 1 inputs and 6 output network tensors.
[09/20/2023-16:57:43] [TRT] [I] Total Host Persistent Memory: 450704
[09/20/2023-16:57:43] [TRT] [I] Total Device Persistent Memory: 1162240
[09/20/2023-16:57:43] [TRT] [I] Total Scratch Memory: 1605632
[09/20/2023-16:57:43] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 42 MiB
[09/20/2023-16:57:43] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 571 steps to complete.
[09/20/2023-16:57:43] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 180.923ms to assign 69 blocks to 571 nodes requiring 78085120 bytes.
[09/20/2023-16:57:43] [TRT] [I] Total Activation Memory: 78085120
[09/20/2023-16:57:43] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2435, GPU 4428 (MiB)
[09/20/2023-16:57:43] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2435, GPU 4438 (MiB)
[09/20/2023-16:57:43] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2435, GPU 4414 (MiB)
[09/20/2023-16:57:43] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2435, GPU 4422 (MiB)
[09/20/2023-16:57:43] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +75, now: CPU 0, GPU 91 (MiB)
[09/20/2023-16:57:43] [TRT] [I] Starting Calibration.
[09/20/2023-16:57:43] [TRT] [E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)
[09/20/2023-16:57:43] [TRT] [E] 3: [engine.cpp::~Engine::298] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::298, condition: mExecutionContextCounter.use_count() == 1. Destroying an engine object before destroying the IExecutionContext objects it created leads to undefined behavior.
)
[09/20/2023-16:57:43] [TRT] [E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )

which I suspect is a result of the wrong number of output tensors being detected for the network.

Inspecting the onnx model that the engine is being built from with polygraphy correctly shows 1 input and 4 outputs:

brett@brett-home:~/Work/Autosensor/NN$ polygraphy inspect model /tmp/model-with-shapes.onnx 
[I] Loading model: /tmp/model-with-shapes.onnx
[I] ==== ONNX Model ====
    Name: main_graph | ONNX Opset: 17
    
    ---- 1 Graph Input(s) ----
    {input [dtype=float32, shape=(1, 3, 512, 896)]}
    
    ---- 4 Graph Output(s) ----
    {scores [dtype=float32, shape=('Min(200, NonMaxSuppression_940_o0__d0)', 2)],
     boxes [dtype=float32, shape=('Min(200, NonMaxSuppression_940_o0__d0)', 4)],
     roi [dtype=float32, shape=('Min(200, NonMaxSuppression_940_o0__d0)', 5)],
     count [dtype=int64, shape=()]}
    
    ---- 163 Initializer(s) ----
    
    ---- 999 Node(s) ----

The model is proprietary, so I can't share it and I don't currently have a minimal repro model, however, I can share the builder script (trt_builder.py) which is based on https://github.com/NVIDIA-AI-IOT/jetson_dla_tutorial#step-7.
I've also checked that calibration with the script works if a toy model (with just a single convolution layer) is used to verify that the DatasetCalibrator class works.

When building the TensorRT engine without quantization (i.e., without int8 calibration), the correct number of network outputs is detected and the engine is built successfully.

brett@brett-home:~/Work/Autosensor/NN$ python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x512x896-complexity:0-statnett-0.5-2023-08-01"
/home/brett/Work/Autosensor/.direnv/python-venv-3.11.5/lib/python3.11/site-packages/torch/__init__.py:1418: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/home/brett/Work/Autosensor/.direnv/python-venv-3.11.5/lib/python3.11/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /tmp/build-via-sdist-9wtz2njt/torch-2.2.0a0+gitfaf3de3/aten/src/ATen/native/TensorShape.cpp:3549.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[09/20/2023-16:59:23] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 537, GPU 4117 (MiB)
[09/20/2023-16:59:26] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1445, GPU +266, now: CPU 2058, GPU 4383 (MiB)
[09/20/2023-16:59:26] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/20/2023-16:59:26] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[09/20/2023-16:59:26] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[09/20/2023-16:59:26] [TRT] [I] Graph optimization time: 0.0266299 seconds.
[09/20/2023-16:59:26] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2080, GPU 4391 (MiB)
[09/20/2023-16:59:26] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2080, GPU 4401 (MiB)
[09/20/2023-16:59:26] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
[09/20/2023-17:00:37] [TRT] [I] Detected 1 inputs and 4 output network tensors.
[09/20/2023-17:00:37] [TRT] [I] Total Host Persistent Memory: 453328
[09/20/2023-17:00:37] [TRT] [I] Total Device Persistent Memory: 18432
[09/20/2023-17:00:37] [TRT] [I] Total Scratch Memory: 1205248
[09/20/2023-17:00:37] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 7 MiB, GPU 105 MiB
[09/20/2023-17:00:37] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 178 steps to complete.
[09/20/2023-17:00:37] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 4.81692ms to assign 10 blocks to 178 nodes requiring 27747328 bytes.
[09/20/2023-17:00:37] [TRT] [I] Total Activation Memory: 27747328
[09/20/2023-17:00:37] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2330, GPU 4427 (MiB)
[09/20/2023-17:00:37] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2331, GPU 4437 (MiB)
[09/20/2023-17:00:37] [TRT] [W] TensorRT encountered issues when converting weights between types and that could affect accuracy.
[09/20/2023-17:00:37] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to adjust the magnitude of the weights.
[09/20/2023-17:00:37] [TRT] [W] Check verbose logs for the list of affected weights.
[09/20/2023-17:00:37] [TRT] [W] - 61 weights are affected by this issue: Detected subnormal FP16 values.
[09/20/2023-17:00:37] [TRT] [W] - 3 weights are affected by this issue: Detected values less than smallest positive FP16 subnormal value and converted them to the FP16 minimum subnormalized value.
[09/20/2023-17:00:37] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +6, GPU +10, now: CPU 6, GPU 10 (MiB)

and polygraphy gives sensible output for the resulting engine:

brett@brett-home:~/Work/Autosensor/NN$ polygraphy inspect model /tmp/model.engine 
[I] Loading bytes from /tmp/model.engine
[I] ==== TensorRT Engine ====
    Name: Unnamed Network 0 | Explicit Batch Engine
    
    ---- 1 Engine Input(s) ----
    {input [dtype=float32, shape=(1, 3, 512, 896)]}
    
    ---- 4 Engine Output(s) ----
    {roi [dtype=float32, shape=(-1, 5)],
     scores [dtype=float32, shape=(-1, 2)],
     boxes [dtype=float32, shape=(-1, 4)],
     count [dtype=int32, shape=()]}
    
    ---- Memory ----
    Device Memory: 27747328 bytes
    
    ---- 1 Profile(s) (5 Tensor(s) Each) ----
    - Profile: 0
        Tensor: input           (Input), Index: 0 | Shapes: min=(1, 3, 512, 896), opt=(1, 3, 512, 896), max=(1, 3, 512, 896)
        Tensor: roi            (Output), Index: 1 | Shape: (-1, 5)
        Tensor: scores         (Output), Index: 2 | Shape: (-1, 2)
        Tensor: boxes          (Output), Index: 3 | Shape: (-1, 4)
        Tensor: count          (Output), Index: 4 | Shape: ()
    
    ---- 215 Layer(s) ----

Environment

Modified collect_env.py script from PyTorch to include tensorrt in the pip packages:

brett@brett-home:~/Work/Autosensor/NN$ python collect_env.py 
Collecting environment information...
PyTorch version: 2.2.0a0
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: Ubuntu Mantic Minotaur (development branch) (x86_64)
GCC version: (Ubuntu 13.2.0-4ubuntu1) 13.2.0
Clang version: 16.0.6 (15)
CMake version: version 3.27.4
Libc version: glibc-2.38

Python version: 3.11.5 (main, Aug 29 2023, 15:31:31) [GCC 13.2.0] (64-bit runtime)
Python platform: Linux-6.5.1-060501-generic-x86_64-with-glibc2.38
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
Vendor ID:                          GenuineIntel
Model name:                         13th Gen Intel(R) Core(TM) i9-13900K
CPU family:                         6
Model:                              183
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          1
Stepping:                           1
CPU(s) scaling MHz:                 83%
CPU max MHz:                        5800.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5990.40
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          896 KiB (24 instances)
L1i cache:                          1.3 MiB (24 instances)
L2 cache:                           32 MiB (12 instances)
L3 cache:                           36 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.2
[pip3] numpy-quaternion==2022.4.3
[pip3] tensorrt==8.6.1.post1
[pip3] tensorrt-bindings==8.6.1
[pip3] tensorrt-libs==8.6.1
[pip3] torch==2.2.0a0+a683bc5
[pip3] torchaudio==2.0.2
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.17.0a0+4cb3d80
[pip3] triton==2.0.0
[conda] Could not collect

The text was updated successfully, but these errors were encountered:

zerollzeng · 2023-09-23T01:35:36Z

[E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)

Looks like an env issue(related to CUDA I think) here, could you please try use our official docker image?

zerollzeng · 2023-09-23T01:37:35Z

Quick try: https://github.com/NVIDIA/TensorRT/tree/main/tools/Polygraphy/examples/cli/convert/01_int8_calibration_in_tensorrt in our official docker.

BrettRyland · 2023-09-25T15:24:30Z

The int8 calibration example works correctly both in and out of the docker image (ignore the timing values as I was compiling code at the same time).

Outside

brett@brett-home:~/Work/Autosensor/NN/tmp/01_int8_calibration_in_tensorrt$ polygraphy convert identity.onnx --int8 \
    --data-loader-script ./data_loader.py \
    --calibration-cache identity_calib.cache \
    -o identity.engine

[I]     Configuring with profiles: [Profile().add('x', min=[1, 1, 2, 2], opt=[1, 1, 2, 2], max=[1, 1, 2, 2])]
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB, TACTIC_DRAM: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Preview Features       | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
    Calibrator             | Calibrator(<generator object load_data at 0x7f7c7f8f4ba0>, cache='identity_calib.cache', BaseClass=<class 'tensorrt_bindings.tensorrt.IInt8EntropyCalibrator2'>)
[I] Saving calibration cache to identity_calib.cache
[I] Finished engine building in 0.869 seconds
brett@brett-home:~/Work/Autosensor/NN/tmp/01_int8_calibration_in_tensorrt$ polygraphy convert identity.onnx --int8 \
    --calibration-cache identity_calib.cache \
    -o identity.engine
[I]     Configuring with profiles: [Profile().add('x', min=[1, 1, 2, 2], opt=[1, 1, 2, 2], max=[1, 1, 2, 2])]
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB, TACTIC_DRAM: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Preview Features       | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
    Calibrator             | Calibrator(DataLoader(seed=1, iterations=1, int_range=(1, 25), float_range=(-1.0, 1.0), val_range=(0.0, 1.0)), cache='identity_calib.cache', BaseClass=<class 'tensorrt_bindings.tensorrt.IInt8EntropyCalibrator2'>)
[I] Loading calibration cache from identity_calib.cache
[I] Finished engine building in 0.384 seconds

Inside

root@d09191f5a11c:/workspace/int8_calibration_example# polygraphy convert identity.onnx --int8 \
>     --data-loader-script ./data_loader.py \
>     --calibration-cache identity_calib.cache \
>     -o identity.engine

[I]     Configuring with profiles: [Profile().add('x', min=[1, 1, 2, 2], opt=[1, 1, 2, 2], max=[1, 1, 2, 2])]
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Calibrator             | Calibrator(<generator object load_data at 0x7f00e1114580>, cache='identity_calib.cache', BaseClass=<class 'tensorrt.tensorrt.IInt8EntropyCalibrator2'>)
[I] Saving calibration cache to identity_calib.cache
[I] Finished engine building in 0.150 seconds
[I] Saving engine to identity.engine
root@d09191f5a11c:/workspace/int8_calibration_example# polygraphy convert identity.onnx --int8 \
>     --calibration-cache identity_calib.cache \
>     -o identity.engine

[I]     Configuring with profiles: [Profile().add('x', min=[1, 1, 2, 2], opt=[1, 1, 2, 2], max=[1, 1, 2, 2])]
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Calibrator             | Calibrator(DataLoader(seed=1, iterations=1, int_range=(1, 25), float_range=(-1.0, 1.0), val_range=(0.0, 1.0)), cache='identity_calib.cache', BaseClass=<class 'tensorrt.tensorrt.IInt8EntropyCalibrator2'>)
[I] Loading calibration cache from identity_calib.cache
[I] Finished engine building in 0.179 seconds
[I] Saving engine to identity.engine

Using the official docker image with pytorch and torchvision built from the latest commits (217b37c023d and 7ba3d7e202 respectively), I still get this Error Code 1: Cask (shader run failed) error when performing int8 calibration and the number of detected outputs is incorrect for int8 calibration.

trt_builder.py script output without and with int8 calibration:

root@d09191f5a11c:/home/brett/Work/Autosensor/NN# python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x384x384-complexity:0-components-2023-09-14-0.2"
2023-09-25 15:16:21,229 log.dl WARNING: One or both of cached dir (None) and originals dir (/storage/media) is None or does not exist, continuing without cache.
/usr/local/lib/python3.8/dist-packages/torch/__init__.py:1436: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /tmp/build-via-sdist-dan8fga9/torch-2.2.0a0+git217b37c/aten/src/ATen/native/TensorShape.cpp:3549.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[09/25/2023-15:16:25] [TRT] [I] [MemUsageChange] Init CUDA: CPU +3, GPU +0, now: CPU 695, GPU 3921 (MiB)
[09/25/2023-15:16:26] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +546, GPU +116, now: CPU 1295, GPU 4037 (MiB)
[09/25/2023-15:16:26] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:375: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/25/2023-15:16:27] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[09/25/2023-15:16:27] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:16:27] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:16:27] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:16:27] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:16:27] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:16:27] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:16:27] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:16:27] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1316, GPU 4045 (MiB)
[09/25/2023-15:16:27] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 1316, GPU 4055 (MiB)
[09/25/2023-15:16:27] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
[09/25/2023-15:17:56] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:17:56] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:17:56] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:17:58] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:17:58] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:17:58] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:18:00] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:18:00] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:18:00] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:18:21] [TRT] [I] Total Activation Memory: 25468879872
[09/25/2023-15:18:21] [TRT] [I] Detected 1 inputs and 4 output network tensors.
[09/25/2023-15:18:21] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:18:21] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:18:21] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:18:21] [TRT] [I] Total Host Persistent Memory: 198128
[09/25/2023-15:18:21] [TRT] [I] Total Device Persistent Memory: 221696
[09/25/2023-15:18:21] [TRT] [I] Total Scratch Memory: 645120
[09/25/2023-15:18:21] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 7 MiB, GPU 1231 MiB
[09/25/2023-15:18:21] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 187 steps to complete.
[09/25/2023-15:18:21] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 5.9263ms to assign 10 blocks to 187 nodes requiring 16813568 bytes.
[09/25/2023-15:18:21] [TRT] [I] Total Activation Memory: 16813568
[09/25/2023-15:18:21] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1947, GPU 4155 (MiB)
[09/25/2023-15:18:21] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 1947, GPU 4163 (MiB)
[09/25/2023-15:18:21] [TRT] [W] TensorRT encountered issues when converting weights between types and that could affect accuracy.
[09/25/2023-15:18:21] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to adjust the magnitude of the weights.
[09/25/2023-15:18:21] [TRT] [W] Check verbose logs for the list of affected weights.
[09/25/2023-15:18:21] [TRT] [W] - 94 weights are affected by this issue: Detected subnormal FP16 values.
[09/25/2023-15:18:21] [TRT] [W] - 40 weights are affected by this issue: Detected values less than smallest positive FP16 subnormal value and converted them to the FP16 minimum subnormalized value.
[09/25/2023-15:18:21] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +7, GPU +7, now: CPU 7, GPU 7 (MiB)

root@d09191f5a11c:/home/brett/Work/Autosensor/NN# python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x384x384-complexity:0-components-2023-09-14-0.2" -q
2023-09-25 15:18:55,862 log.dl WARNING: One or both of cached dir (None) and originals dir (/storage/media) is None or does not exist, continuing without cache.
/usr/local/lib/python3.8/dist-packages/torch/__init__.py:1436: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/usr/local/lib/python3.8/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /tmp/build-via-sdist-dan8fga9/torch-2.2.0a0+git217b37c/aten/src/ATen/native/TensorShape.cpp:3549.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[09/25/2023-15:18:59] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 698, GPU 3957 (MiB)
[09/25/2023-15:19:00] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +546, GPU +116, now: CPU 1299, GPU 4073 (MiB)
[09/25/2023-15:19:01] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:375: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/25/2023-15:19:01] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[09/25/2023-15:19:01] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:19:01] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:19:01] [TRT] [W] parsers/onnx/onnx2trt_utils.cpp:403: One or more weights outside the range of INT32 was clamped
[09/25/2023-15:19:01] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:19:01] [TRT] [W] Using PreviewFeature::kFASTER_DYNAMIC_SHAPES_0805 can help improve performance and resolve potential functional issues.
[09/25/2023-15:19:01] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1320, GPU 4081 (MiB)
[09/25/2023-15:19:01] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 1321, GPU 4091 (MiB)
[09/25/2023-15:19:01] [TRT] [I] Timing cache disabled. Turning it on will improve builder speed.
[09/25/2023-15:19:04] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:19:04] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:19:04] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:19:08] [TRT] [I] Total Activation Memory: 25643102208
[09/25/2023-15:19:08] [TRT] [I] Detected 1 inputs and 6 output network tensors.
[09/25/2023-15:19:08] [TRT] [W] Myelin graph with multiple dynamic values may have poor performance if they differ. Dynamic values are: 
[09/25/2023-15:19:08] [TRT] [W]  (# 0 (VALUE (Unnamed Layer* 1500) [NMS]_1_output))
[09/25/2023-15:19:08] [TRT] [W]  (# 0 (VALUE /NonZero[size]))
[09/25/2023-15:19:08] [TRT] [I] Total Host Persistent Memory: 132032
[09/25/2023-15:19:08] [TRT] [I] Total Device Persistent Memory: 0
[09/25/2023-15:19:08] [TRT] [I] Total Scratch Memory: 645120
[09/25/2023-15:19:08] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 14 MiB
[09/25/2023-15:19:08] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 576 steps to complete.
[09/25/2023-15:19:08] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 171.831ms to assign 69 blocks to 576 nodes requiring 25499136 bytes.
[09/25/2023-15:19:08] [TRT] [I] Total Activation Memory: 25499136
[09/25/2023-15:19:08] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1565, GPU 4117 (MiB)
[09/25/2023-15:19:08] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +8, now: CPU 1566, GPU 4125 (MiB)
[09/25/2023-15:19:08] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 1565, GPU 4101 (MiB)
[09/25/2023-15:19:08] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 1565, GPU 4109 (MiB)
[09/25/2023-15:19:08] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +24, now: CPU 0, GPU 38 (MiB)
[09/25/2023-15:19:08] [TRT] [I] Starting Calibration.
[09/25/2023-15:19:08] [TRT] [E] 2: [helpers.h::divUp::70] Error Code 2: Internal Error (Assertion n > 0 failed. )
[09/25/2023-15:19:08] [TRT] [E] 3: [engine.cpp::~Engine::307] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::307, condition: mObjectCounter.use_count() == 1. Destroying an engine object before destroying objects it created leads to undefined behavior.
)
[09/25/2023-15:19:08] [TRT] [E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )
[09/25/2023-15:19:08] [TRT] [E] 2: [builder.cpp::buildSerializedNetwork::751] Error Code 2: Internal Error (Assertion engine != nullptr failed. )

Updated environment

root@d09191f5a11c:/home/brett/Work/Autosensor/NN# python collect_env.py 
Collecting environment information...
PyTorch version: 2.2.0a0+git217b37c
Is debug build: False
CUDA used to build PyTorch: 12.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.27.5
Libc version: glibc-2.31

Python version: 3.8.10 (default, Nov 14 2022, 12:59:47)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-6.5.1-060501-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             32
On-line CPU(s) list:                0-31
Thread(s) per core:                 1
Core(s) per socket:                 24
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              183
Model name:                         13th Gen Intel(R) Core(TM) i9-13900K
Stepping:                           1
CPU MHz:                            4225.442
CPU max MHz:                        5800.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5990.40
Virtualization:                     VT-x
L1d cache:                          576 KiB
L1i cache:                          384 KiB
L2 cache:                           24 MiB
NUMA node0 CPU(s):                  0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] numpy-quaternion==2022.4.3
[pip3] tensorrt==8.5.2.2
[pip3] torch==2.2.0a0+git217b37c
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.17.0a0+7ba3d7e
[pip3] triton==2.0.0
[conda] Could not collect

zerollzeng · 2023-09-30T09:21:16Z

Looks like a cuda conflict between pytorch and tensorrt. we have pytorch docker in NGC, could you pease try that? Thanks!

BrettRyland · 2023-10-02T09:56:48Z

That doesn't appear to make any difference.

Setup of docker image with extra installs required for the model:

brett@brett-home:~/Work$ docker run --gpus all -it -v ./Autosensor:/home/brett/Work/Autosensor -v /usr/local/media:/usr/local/media --rm nvcr.io/nvidia/pytorch:23.09-py3

=============
== PyTorch ==
=============

NVIDIA Release 23.09 (build 69180607)
PyTorch Version 2.1.0a0+32f93b1

Container image Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Copyright (c) 2014-2023 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...

root@cf9b065a0457:/workspace# apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get install -qq ffmpeg libffms2-5
Extracting templates from packages: 100%
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invoke-rc.d: could not determine current runlevel
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Regenerating fonts cache... done.
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Setting up libffms2-5:amd64 (2.40+git20211209-2) ...
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Setting up ffmpeg (7:4.4.2-0ubuntu0.22.04.1) ...
Processing triggers for libc-bin (2.35-0ubuntu3.1) ...
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root@cf9b065a0457:/workspace# pip install ffms2 ffmpeg-python onnxruntime pympler deform_conv2d_onnx_exporter
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting ffms2
  Downloading ffms2-0.4.5.5.tar.gz (693 kB)
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Without int8 calibration, the 4 outputs are correctly detected:

root@cf9b065a0457:/home/brett/Work/Autosensor/NN# python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x512x896-complexity:0-statnett-0.5-2023-08-01" 
/usr/local/lib/python3.10/dist-packages/torchvision/datapoints/__init__.py:14: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
/usr/local/lib/python3.10/dist-packages/torchvision/transforms/v2/__init__.py:64: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
2023-10-02 09:45:12,512 log.dl WARNING: One or both of cached dir (/storage/media/cache_512p) and originals dir (/storage/media) is None or does not exist, continuing without cache.
/usr/local/lib/python3.10/dist-packages/torch/__init__.py:1383: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3516.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[10/02/2023-09:45:16] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 535, GPU 4640 (MiB)
[10/02/2023-09:45:19] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1445, GPU +264, now: CPU 2056, GPU 4904 (MiB)
[10/02/2023-09:45:19] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[10/02/2023-09:45:19] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[10/02/2023-09:45:19] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[10/02/2023-09:45:19] [TRT] [I] Graph optimization time: 0.0260609 seconds.
[10/02/2023-09:45:19] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +1, GPU +8, now: CPU 2079, GPU 4912 (MiB)
[10/02/2023-09:45:19] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2079, GPU 4922 (MiB)
[10/02/2023-09:45:19] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
[10/02/2023-09:46:26] [TRT] [I] Detected 1 inputs and 4 output network tensors.
[10/02/2023-09:46:26] [TRT] [I] Total Host Persistent Memory: 458112
[10/02/2023-09:46:26] [TRT] [I] Total Device Persistent Memory: 17408
[10/02/2023-09:46:26] [TRT] [I] Total Scratch Memory: 1376256
[10/02/2023-09:46:26] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 7 MiB, GPU 105 MiB
[10/02/2023-09:46:26] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 179 steps to complete.
[10/02/2023-09:46:26] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 5.02352ms to assign 10 blocks to 179 nodes requiring 27747328 bytes.
[10/02/2023-09:46:26] [TRT] [I] Total Activation Memory: 27747328
[10/02/2023-09:46:26] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2324, GPU 4921 (MiB)
[10/02/2023-09:46:26] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2325, GPU 4931 (MiB)
[10/02/2023-09:46:26] [TRT] [W] TensorRT encountered issues when converting weights between types and that could affect accuracy.
[10/02/2023-09:46:26] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to adjust the magnitude of the weights.
[10/02/2023-09:46:26] [TRT] [W] Check verbose logs for the list of affected weights.
[10/02/2023-09:46:26] [TRT] [W] - 61 weights are affected by this issue: Detected subnormal FP16 values.
[10/02/2023-09:46:26] [TRT] [W] - 3 weights are affected by this issue: Detected values less than smallest positive FP16 subnormal value and converted them to the FP16 minimum subnormalized value.
[10/02/2023-09:46:26] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +6, GPU +10, now: CPU 6, GPU 10 (MiB)

With int8 calibration, 6 outputs are detected and "Cask (shader run failed)" error occurs:

root@cf9b065a0457:/home/brett/Work/Autosensor/NN# python -- trt_builder.py "saved/RPN_ThunderNet2-activation:BN-ReLU-classes:2-input:3x512x896-complexity:0-statnett-0.5-2023-08-01" -q
/usr/local/lib/python3.10/dist-packages/torchvision/datapoints/__init__.py:14: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
/usr/local/lib/python3.10/dist-packages/torchvision/transforms/v2/__init__.py:64: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
2023-10-02 09:44:36,238 log.dl WARNING: One or both of cached dir (/storage/media/cache_512p) and originals dir (/storage/media) is None or does not exist, continuing without cache.
/usr/local/lib/python3.10/dist-packages/torch/__init__.py:1383: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3516.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
[10/02/2023-09:44:40] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 532, GPU 4697 (MiB)
[10/02/2023-09:44:43] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1445, GPU +266, now: CPU 2053, GPU 4920 (MiB)
[10/02/2023-09:44:43] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[10/02/2023-09:44:43] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[10/02/2023-09:44:43] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[10/02/2023-09:44:43] [TRT] [I] Graph optimization time: 0.00543541 seconds.
[10/02/2023-09:44:43] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2076, GPU 4928 (MiB)
[10/02/2023-09:44:43] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2076, GPU 4938 (MiB)
[10/02/2023-09:44:43] [TRT] [I] Timing cache disabled. Turning it on will improve builder speed.
[10/02/2023-09:44:45] [TRT] [I] Detected 1 inputs and 6 output network tensors.
[10/02/2023-09:44:46] [TRT] [I] Total Host Persistent Memory: 450704
[10/02/2023-09:44:46] [TRT] [I] Total Device Persistent Memory: 1162240
[10/02/2023-09:44:46] [TRT] [I] Total Scratch Memory: 1605632
[10/02/2023-09:44:46] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 42 MiB
[10/02/2023-09:44:46] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 571 steps to complete.
[10/02/2023-09:44:46] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 179.333ms to assign 69 blocks to 571 nodes requiring 78085120 bytes.
[10/02/2023-09:44:46] [TRT] [I] Total Activation Memory: 78085120
[10/02/2023-09:44:46] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2426, GPU 4953 (MiB)
[10/02/2023-09:44:46] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2426, GPU 4963 (MiB)
[10/02/2023-09:44:46] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2426, GPU 4939 (MiB)
[10/02/2023-09:44:46] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2426, GPU 4947 (MiB)
[10/02/2023-09:44:46] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +75, now: CPU 0, GPU 91 (MiB)
[10/02/2023-09:44:46] [TRT] [I] Starting Calibration.
[10/02/2023-09:44:48] [TRT] [E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)
[10/02/2023-09:44:48] [TRT] [E] 3: [engine.cpp::~Engine::298] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::298, condition: mExecutionContextCounter.use_count() == 1. Destroying an engine object before destroying the IExecutionContext objects it created leads to undefined behavior.
)
[10/02/2023-09:44:48] [TRT] [E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )

BrettRyland · 2023-10-02T13:37:41Z

I've put together a repro script (int8_calibration_bug.py) that causes the Cask (shader run failed) error in the pytorch docker from NGC.
Unlike the full model though, the correct number of outputs are detected in this case, so that might be a separate issue.

Without int8 calibration:

root@9c563ec4abcc:/home/brett/Work/Autosensor/NN/tmp# python3 -- int8_calibration_bug.py 
/home/brett/Work/Autosensor/NN/tmp/int8_calibration_bug.py:91: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if roi.shape[0] == 0:
/usr/local/lib/python3.10/dist-packages/torch/__init__.py:1383: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
[10/02/2023-13:31:04] [TRT] [I] [MemUsageChange] Init CUDA: CPU +1, GPU +0, now: CPU 542, GPU 4072 (MiB)
[10/02/2023-13:31:07] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1445, GPU +366, now: CPU 2064, GPU 4438 (MiB)
[10/02/2023-13:31:07] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[10/02/2023-13:31:07] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[10/02/2023-13:31:07] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[10/02/2023-13:31:07] [TRT] [I] Graph optimization time: 0.0011824 seconds.
[10/02/2023-13:31:07] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2065, GPU 4446 (MiB)
[10/02/2023-13:31:07] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2065, GPU 4456 (MiB)
[10/02/2023-13:31:07] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
[10/02/2023-13:31:16] [TRT] [I] Detected 1 inputs and 3 output network tensors.
[10/02/2023-13:31:16] [TRT] [I] Total Host Persistent Memory: 58064
[10/02/2023-13:31:16] [TRT] [I] Total Device Persistent Memory: 0
[10/02/2023-13:31:16] [TRT] [I] Total Scratch Memory: 7168
[10/02/2023-13:31:16] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 4 MiB
[10/02/2023-13:31:16] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 15 steps to complete.
[10/02/2023-13:31:16] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 0.042411ms to assign 5 blocks to 15 nodes requiring 70144 bytes.
[10/02/2023-13:31:16] [TRT] [I] Total Activation Memory: 70144
[10/02/2023-13:31:16] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2252, GPU 4461 (MiB)
[10/02/2023-13:31:16] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2252, GPU 4471 (MiB)
[10/02/2023-13:31:16] [TRT] [W] TensorRT encountered issues when converting weights between types and that could affect accuracy.
[10/02/2023-13:31:16] [TRT] [W] If this is not the desired behavior, please modify the weights or retrain with regularization to adjust the magnitude of the weights.
[10/02/2023-13:31:16] [TRT] [W] Check verbose logs for the list of affected weights.
[10/02/2023-13:31:16] [TRT] [W] - 7 weights are affected by this issue: Detected subnormal FP16 values.
[10/02/2023-13:31:16] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in building engine: CPU +0, GPU +4, now: CPU 0, GPU 4 (MiB)

With int8 calibration:

root@9c563ec4abcc:/home/brett/Work/Autosensor/NN/tmp# python3 -- int8_calibration_bug.py -q
/home/brett/Work/Autosensor/NN/tmp/int8_calibration_bug.py:91: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if roi.shape[0] == 0:
/usr/local/lib/python3.10/dist-packages/torch/__init__.py:1383: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
[10/02/2023-13:31:26] [TRT] [I] [MemUsageChange] Init CUDA: CPU +2, GPU +0, now: CPU 542, GPU 4068 (MiB)
[10/02/2023-13:31:29] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1445, GPU +268, now: CPU 2063, GPU 4336 (MiB)
[10/02/2023-13:31:29] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[10/02/2023-13:31:29] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[10/02/2023-13:31:29] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[10/02/2023-13:31:29] [TRT] [I] Graph optimization time: 0.000634619 seconds.
[10/02/2023-13:31:29] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2064, GPU 4344 (MiB)
[10/02/2023-13:31:29] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2064, GPU 4354 (MiB)
[10/02/2023-13:31:29] [TRT] [I] Timing cache disabled. Turning it on will improve builder speed.
[10/02/2023-13:31:30] [TRT] [I] Detected 1 inputs and 3 output network tensors.
[10/02/2023-13:31:30] [TRT] [I] Total Host Persistent Memory: 61280
[10/02/2023-13:31:30] [TRT] [I] Total Device Persistent Memory: 0
[10/02/2023-13:31:30] [TRT] [I] Total Scratch Memory: 8192
[10/02/2023-13:31:30] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 4 MiB
[10/02/2023-13:31:30] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 27 steps to complete.
[10/02/2023-13:31:30] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 0.1942ms to assign 7 blocks to 27 nodes requiring 105984 bytes.
[10/02/2023-13:31:30] [TRT] [I] Total Activation Memory: 105984
[10/02/2023-13:31:30] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2113, GPU 4368 (MiB)
[10/02/2023-13:31:30] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2113, GPU 4378 (MiB)
[10/02/2023-13:31:30] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2113, GPU 4354 (MiB)
[10/02/2023-13:31:30] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2113, GPU 4362 (MiB)
[10/02/2023-13:31:30] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 4 (MiB)
[10/02/2023-13:31:30] [TRT] [I] Starting Calibration.
[10/02/2023-13:31:30] [TRT] [E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)
[10/02/2023-13:31:30] [TRT] [E] 3: [engine.cpp::~Engine::298] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::298, condition: mExecutionContextCounter.use_count() == 1. Destroying an engine object before destroying the IExecutionContext objects it created leads to undefined behavior.
)
[10/02/2023-13:31:30] [TRT] [E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )

zerollzeng · 2023-10-12T14:13:50Z

The int8 calibration example works correctly both in and out of the docker image (ignore the timing values as I was compiling code at the same time).

If polygraphy works well then this seems a bug in your code. Actually you can use polygraphy to do the calibration and build the engine. Could you please try this?

BrettRyland · 2023-10-15T20:51:30Z

If polygraphy works well then this seems a bug in your code. Actually you can use polygraphy to do the calibration and build the engine. Could you please try this?

I will try this soon, some other more pressing matters came up in the meantime.
Did you try running the repro script I posted above that causes the Cask (shader run failed) error? It doesn't cause the incorrect number of outputs detected issue, but it does cause that error, so it's something you should be able to test locally.

Demonstration of repro script with NGC docker container:

brett@brett-home:~/Work/Autosensor/NN/tmp$ docker run --gpus all -it -v "$(pwd)":/workspace/tmp -v /usr/local/media:/usr/local/media --rm nvcr.io/nvidia/pytorch:23.09-py3

=============
== PyTorch ==
=============

NVIDIA Release 23.09 (build 69180607)
PyTorch Version 2.1.0a0+32f93b1

Container image Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Copyright (c) 2014-2023 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...

root@aca30650930b:/workspace# cd tmp/
root@aca30650930b:/workspace/tmp# pip install onnxruntime
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting onnxruntime
  Obtaining dependency information for onnxruntime from https://files.pythonhosted.org/packages/e0/c1/236fe9621584f32ffd6c1eeba4005f1c2932d2e21ef901c321ae51b9219d/onnxruntime-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata
  Downloading onnxruntime-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Collecting coloredlogs (from onnxruntime)
  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.0/46.0 kB 2.8 MB/s eta 0:00:00
Collecting flatbuffers (from onnxruntime)
  Obtaining dependency information for flatbuffers from https://files.pythonhosted.org/packages/6f/12/d5c79ee252793ffe845d58a913197bfa02ae9a0b5c9bc3dc4b58d477b9e7/flatbuffers-23.5.26-py2.py3-none-any.whl.metadata
  Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.22.2)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (23.1)
Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (4.24.3)
Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from onnxruntime) (1.12)
Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime)
  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 86.8/86.8 kB 11.7 MB/s eta 0:00:00
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime) (1.3.0)
Downloading onnxruntime-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.2 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 26.9 MB/s eta 0:00:00
Downloading flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)
Installing collected packages: flatbuffers, humanfriendly, coloredlogs, onnxruntime
Successfully installed coloredlogs-15.0.1 flatbuffers-23.5.26 humanfriendly-10.0 onnxruntime-1.16.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.2.1 -> 23.3
[notice] To update, run: python -m pip install --upgrade pip
root@aca30650930b:/workspace/tmp# python3 -- int8_calibration_bug.py -q
/workspace/tmp/int8_calibration_bug.py:91: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if roi.shape[0] == 0:  # Avoid convolutions on empty tensors.
/usr/local/lib/python3.10/dist-packages/torch/__init__.py:1383: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert condition, message
[10/15/2023-20:46:14] [TRT] [I] [MemUsageChange] Init CUDA: CPU +1, GPU +0, now: CPU 543, GPU 4298 (MiB)
[10/15/2023-20:46:17] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +1444, GPU +258, now: CPU 2064, GPU 4548 (MiB)
[10/15/2023-20:46:17] [TRT] [W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[10/15/2023-20:46:17] [TRT] [W] Tensor DataType is determined at build time for tensors not marked as input or output.
[10/15/2023-20:46:17] [TRT] [W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[10/15/2023-20:46:17] [TRT] [I] Graph optimization time: 0.000654949 seconds.
[10/15/2023-20:46:17] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2066, GPU 4556 (MiB)
[10/15/2023-20:46:17] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +10, now: CPU 2066, GPU 4566 (MiB)
[10/15/2023-20:46:17] [TRT] [I] Timing cache disabled. Turning it on will improve builder speed.
[10/15/2023-20:46:18] [TRT] [I] Detected 1 inputs and 3 output network tensors.
[10/15/2023-20:46:18] [TRT] [I] Total Host Persistent Memory: 61280
[10/15/2023-20:46:18] [TRT] [I] Total Device Persistent Memory: 0
[10/15/2023-20:46:18] [TRT] [I] Total Scratch Memory: 8192
[10/15/2023-20:46:18] [TRT] [I] [MemUsageStats] Peak memory usage of TRT CPU/GPU memory allocators: CPU 0 MiB, GPU 4 MiB
[10/15/2023-20:46:18] [TRT] [I] [BlockAssignment] Started assigning block shifts. This will take 27 steps to complete.
[10/15/2023-20:46:18] [TRT] [I] [BlockAssignment] Algorithm ShiftNTopDown took 0.203276ms to assign 7 blocks to 27 nodes requiring 105984 bytes.
[10/15/2023-20:46:18] [TRT] [I] Total Activation Memory: 105984
[10/15/2023-20:46:18] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2114, GPU 4579 (MiB)
[10/15/2023-20:46:18] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +1, GPU +10, now: CPU 2115, GPU 4589 (MiB)
[10/15/2023-20:46:18] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +0, GPU +8, now: CPU 2114, GPU 4565 (MiB)
[10/15/2023-20:46:18] [TRT] [I] [MemUsageChange] Init cuDNN: CPU +0, GPU +8, now: CPU 2114, GPU 4573 (MiB)
[10/15/2023-20:46:18] [TRT] [I] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +0, now: CPU 0, GPU 4 (MiB)
[10/15/2023-20:46:18] [TRT] [I] Starting Calibration.
[10/15/2023-20:46:18] [TRT] [E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)
[10/15/2023-20:46:18] [TRT] [E] 3: [engine.cpp::~Engine::298] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::298, condition: mExecutionContextCounter.use_count() == 1. Destroying an engine object before destroying the IExecutionContext objects it created leads to undefined behavior.
)
[10/15/2023-20:46:18] [TRT] [E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )

BrettRyland · 2023-10-16T10:15:39Z

If polygraphy works well then this seems a bug in your code. Actually you can use polygraphy to do the calibration and build the engine. Could you please try this?

Using polygraphy for int8 calibration of the full model gives the same error as the self-contained repro script I posted above (the extra installs are required for the custom dataloader):

brett@brett-home:~/Work/Autosensor/NN/tmp/trt-issue-3339$ docker run --gpus all -it -v "$(pwd)/../..":/workspace/Autosensor -v /usr/local/media:/usr/local/media --rm nvcr.io/nvidia/pytorch:23.09-py3

=============
== PyTorch ==
=============

NVIDIA Release 23.09 (build 69180607)
PyTorch Version 2.1.0a0+32f93b1

Container image Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Copyright (c) 2014-2023 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...

root@8cc7447a82cc:/workspace# cd Autosensor/tmp/trt-issue-3339 && pip install -r requirements-for-docker.txt && apt-get -qq update && apt-get install -qq -y libffms2-5 ffmpeg && rm -f *.engine *.cache && ls
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Collecting ffmpeg-python (from -r requirements-for-docker.txt (line 2))
  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)
Collecting ffms2 (from -r requirements-for-docker.txt (line 3))
  Downloading ffms2-0.4.5.5.tar.gz (693 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 693.2/693.2 kB 16.1 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting pympler (from -r requirements-for-docker.txt (line 4))
  Downloading Pympler-1.0.1-py3-none-any.whl (164 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 164.8/164.8 kB 427.3 MB/s eta 0:00:00
Collecting onnxruntime (from -r requirements-for-docker.txt (line 7))
  Obtaining dependency information for onnxruntime from https://files.pythonhosted.org/packages/e0/c1/236fe9621584f32ffd6c1eeba4005f1c2932d2e21ef901c321ae51b9219d/onnxruntime-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata
  Downloading onnxruntime-1.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)
Collecting deform_conv2d_onnx_exporter (from -r requirements-for-docker.txt (line 8))
  Downloading deform_conv2d_onnx_exporter-1.2.0-py3-none-any.whl (8.5 kB)
Collecting colored (from -r requirements-for-docker.txt (line 9))
  Obtaining dependency information for colored from https://files.pythonhosted.org/packages/6f/0d/a10351ef1a98e0b03d66887ec2d87c261f9a0fbff8f2bdb75614cc0a2850/colored-2.2.3-py3-none-any.whl.metadata
  Downloading colored-2.2.3-py3-none-any.whl.metadata (3.6 kB)
Collecting future (from ffmpeg-python->-r requirements-for-docker.txt (line 2))
  Downloading future-0.18.3.tar.gz (840 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 840.9/840.9 kB 71.0 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from ffms2->-r requirements-for-docker.txt (line 3)) (1.22.2)
Collecting coloredlogs (from onnxruntime->-r requirements-for-docker.txt (line 7))
  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)
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Collecting flatbuffers (from onnxruntime->-r requirements-for-docker.txt (line 7))
  Obtaining dependency information for flatbuffers from https://files.pythonhosted.org/packages/6f/12/d5c79ee252793ffe845d58a913197bfa02ae9a0b5c9bc3dc4b58d477b9e7/flatbuffers-23.5.26-py2.py3-none-any.whl.metadata
  Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from onnxruntime->-r requirements-for-docker.txt (line 7)) (23.1)
Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime->-r requirements-for-docker.txt (line 7)) (4.24.3)
Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from onnxruntime->-r requirements-for-docker.txt (line 7)) (1.12)
Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (2.1.0a0+32f93b1)
Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (0.16.0a0)
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Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (4.7.1)
Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (2.6.3)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (3.1.2)
Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (2023.6.0)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision>=0.9.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (2.31.0)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision>=0.9.0->deform_conv2d_onnx_exporter->-r requirements-for-docker.txt (line 8)) (9.2.0)
Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime->-r requirements-for-docker.txt (line 7))
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Building wheels for collected packages: ffms2, future
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Successfully built ffms2 future
Installing collected packages: flatbuffers, pympler, humanfriendly, future, ffms2, colored, ffmpeg-python, coloredlogs, onnxruntime, deform_conv2d_onnx_exporter
Successfully installed colored-2.2.3 coloredlogs-15.0.1 deform_conv2d_onnx_exporter-1.2.0 ffmpeg-python-0.2.0 ffms2-0.4.5.5 flatbuffers-23.5.26 future-0.18.3 humanfriendly-10.0 onnxruntime-1.16.1 pympler-1.0.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.2.1 -> 23.3
[notice] To update, run: python -m pip install --upgrade pip
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debconf: unable to initialize frontend: Dialog
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BR_logging.py  __pycache__  autosensor  dataloader_video.py  model.onnx  model.state  requirements-for-docker.txt  trt_builder.py  trt_dataloader.py  trt_infer.py  utils.py

root@8cc7447a82cc:/workspace/Autosensor/tmp/trt-issue-3339# polygraphy convert model.onnx -o model-fp16.engine
[W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[W] Tensor DataType is determined at build time for tensors not marked as input or output.
[W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[I] Configuring with profiles:[
        Profile 0:
            {input [min=[1, 3, 384, 384], opt=[1, 3, 384, 384], max=[1, 3, 384, 384]]}
    ]
[I] Building engine with configuration:
    Flags                  | []
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB, TACTIC_DRAM: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Preview Features       | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
[I] Finished engine building in 117.011 seconds

root@8cc7447a82cc:/workspace/Autosensor/tmp/trt-issue-3339# polygraphy convert model.onnx --int8 --data-loader-script ./trt_dataloader.py --calibration-cache calibration.cache -o model-int8.engine
/usr/local/lib/python3.10/dist-packages/torchvision/datapoints/__init__.py:14: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
/usr/local/lib/python3.10/dist-packages/torchvision/transforms/v2/__init__.py:64: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
[W] onnx2trt_utils.cpp:374: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[W] Tensor DataType is determined at build time for tensors not marked as input or output.
[W] onnx2trt_utils.cpp:400: One or more weights outside the range of INT32 was clamped
[I] Configuring with profiles:[
        Profile 0:
            {input [min=[1, 3, 384, 384], opt=[1, 3, 384, 384], max=[1, 3, 384, 384]]}
    ]
[I] Using calibration profile: {input [min=[1, 3, 384, 384], opt=[1, 3, 384, 384], max=[1, 3, 384, 384]]}
[I] Building engine with configuration:
    Flags                  | [INT8]
    Engine Capability      | EngineCapability.DEFAULT
    Memory Pools           | [WORKSPACE: 24214.56 MiB, TACTIC_DRAM: 24214.56 MiB]
    Tactic Sources         | [CUBLAS, CUBLAS_LT, CUDNN, EDGE_MASK_CONVOLUTIONS, JIT_CONVOLUTIONS]
    Profiling Verbosity    | ProfilingVerbosity.DETAILED
    Preview Features       | [FASTER_DYNAMIC_SHAPES_0805, DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
    Calibrator             | Calibrator(<generator object load_data at 0x7f0ed217fdf0>, cache='calibration.cache', BaseClass=<class 'tensorrt.tensorrt.IInt8EntropyCalibrator2'>)
2023-10-16 09:30:55,589 log.dl WARNING: One or both of cached dir (None) and originals dir (/storage/media) is None or does not exist, continuing without cache.
[E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)
[E] 3: [engine.cpp::~Engine::298] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/engine.cpp::~Engine::298, condition: mExecutionContextCounter.use_count() == 1. Destroying an engine object before destroying the IExecutionContext objects it created leads to undefined behavior.
    )
[E] 2: [calibrator.cpp::calibrateEngine::1181] Error Code 2: Internal Error (Assertion context->executeV2(&bindings[0]) failed. )
[!] Invalid Engine. Please ensure the engine was built correctly

Demonstration of dataloader script providing appropriate data:

root@b3bfd418437e:/workspace/Autosensor/tmp/trt-issue-3339# python3
Python 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from trt_dataloader import load_data
/usr/local/lib/python3.10/dist-packages/torchvision/datapoints/__init__.py:14: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
/usr/local/lib/python3.10/dist-packages/torchvision/transforms/v2/__init__.py:64: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().
  warnings.warn(_BETA_TRANSFORMS_WARNING)
>>> for i, data in enumerate(load_data()):
...   print(type(data), data.keys(), type(data['input']), data['input'].shape, data['input'].dtype)
...   if i > 4: break
... 
2023-10-16 10:00:40,942 log.dl WARNING: One or both of cached dir (None) and originals dir (/storage/media) is None or does not exist, continuing without cache.
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32
<class 'dict'> dict_keys(['input']) <class 'numpy.ndarray'> (1, 3, 384, 384) float32

Based on the filename of where the error occurs (softMaxV2Runner.cpp), I expect that this error originates from the onnx equivalent of the torch.nn.functional.softmax call in the region proposal section of the network (in fwd_rpn in the repro script).

BrettRyland · 2023-10-16T11:15:23Z

An even simpler repro script: int8_calibration_bug_simpler_repro.py, run with python -- int8_calibration_bug_simpler_repro.py -q

Here the model (which uses non-sensical data, but represents part of a region proposal section of a full RPN-style network) is simply

class MyModel(torch.nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.input_size = [3, 64, 64]

    def forward(self, x):
        N = x.shape[0]
        scores = x.reshape(N, -1, 2, 2)[:, :, 0]
        boxes = x.reshape(N, -1, 4)
        prediction_mask = [torch.argmax(score, dim=1) for score in scores.unbind(dim=0)]
        proposals = [box[index] for box, index in zip(boxes.unbind(dim=0), prediction_mask)]  # Select the foreground boxes as proposals.
        scores = [score[mask, 0] for score, mask in zip(torch.nn.functional.softmax(scores, dim=-1).unbind(dim=0), prediction_mask)]  # Get their normalised scores.
        keeps = [torchvision.ops.nms(boxes, scores, iou_threshold=0.7)[:20] for boxes, scores in zip(proposals, scores)]  # Perform non-maximum suppression on each item in the batch. Take only the top 20 from each.
        boxes = [proposal[keep] for proposal, keep in zip(proposals, keeps)]  # Commenting out this line prevents the "[softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)" error.
        return x, scores, boxes

The [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed) error occurs due to the final boxes = ... line of the forward function.

zerollzeng · 2023-10-20T07:14:20Z

I've seen a similar issue recently, what's your GPU driver version?

zerollzeng · 2023-10-20T07:36:42Z

I can reproduce the issue with int8_calibration_bug_simpler_repro.py. I'll file internal bug to track this later.

BrettRyland · 2023-10-20T07:41:46Z

I've seen a similar issue recently, what's your GPU driver version?

The driver version has been updated since the original post, which was Nvidia driver version: 535.104.05. It's currently 535.113.01:

$ sudo inxi --graphics 
Graphics:
  Device-1: NVIDIA AD102 [GeForce RTX 4090] driver: nvidia v: 535.113.01
  Device-2: Logitech HD Pro Webcam C920 driver: snd-usb-audio,uvcvideo
    type: USB
  Display: server: X.Org v: 1.21.1.7 with: Xwayland v: 23.2.0 driver: X:
    loaded: nvidia unloaded: fbdev,modesetting,nouveau,vesa
    gpu: nvidia,nvidia-nvswitch resolution: 1: 3840x2160~60Hz
    2: 3840x2160~60Hz 3: 3840x2160~60Hz 4: 3840x2160~60Hz
  API: OpenGL v: 4.6.0 NVIDIA 535.113.01 renderer: NVIDIA GeForce RTX
    4090/PCIe/SSE2

zerollzeng · 2023-10-20T07:50:42Z

Thanks, I've filed internal bug 4340507 to track this, sorry about the delayed response, quite busy with other things these day :-)

zerollzeng · 2024-02-20T06:59:00Z

Issue is fixed in TRT 10. Closed.

lix19937 · 2024-04-21T01:28:40Z

[E] 1: [softMaxV2Runner.cpp::execute::226] Error Code 1: Cask (shader run failed)

usually binary compile not with right sm arch

zerollzeng self-assigned this Sep 23, 2023

zerollzeng added the triaged Issue has been triaged by maintainers label Sep 23, 2023

zerollzeng mentioned this issue Nov 18, 2023

Cask (shader run failed) #3459

Open

ttyio added the internal-bug-tracked Tracked internally, will be fixed in a future release. label Jan 16, 2024

zerollzeng closed this as completed Feb 20, 2024

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Incorrect number of network outputs detected during int8 calibration leading to failure #3339

Incorrect number of network outputs detected during int8 calibration leading to failure #3339

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Incorrect number of network outputs detected during int8 calibration leading to failure #3339

Incorrect number of network outputs detected during int8 calibration leading to failure #3339

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