🐛 [Bug] Could not implicitly convert NumPy data type: i64 to TensorRT

[dudeperf3ct]

Bug Description

TensorRT engine produces error when ran on Jetson for fcn_resnet model. However, it does not produce error when ran on desktop.

Dynamo frontend is used for creating a TensorRT engine.

Error : [TRT] [E] Could not implicitly convert NumPy data type: i64 to TensorRT.

To Reproduce

Steps to reproduce the behavior:

The following are relevant code for loading and converting to a TensorRT model.

input_data = torch.randn(args.input_shape, device=DEVICE)
model = torch.hub.load("pytorch/vision", 'fcn_resnet50', pretrained=True)
model.eval().to(DEVICE)

input_data = input_data.to(torch.float16)
model = model.to(torch.float16)

exp_program = torch.export.export(model, tuple([input_data]))
model = torch_tensorrt.dynamo.compile(
    exported_program=exp_program,
    inputs=[input_data],
    min_block_size=args.min_block_size,
    optimization_level=args.optimization_level,
    enabled_precisions={dtype},
    # Set to True for verbose output
    # NOTE: Performance Regression when rich library is available
    # https://github.com/pytorch/TensorRT/issues/3215
    debug=True,
    # Setting it to True returns PythonTorchTensorRTModule which has different profiling approach
    use_python_runtime=True,
)

for _ in range(100):
    _ = model(input)

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

Jetson Orion Developer Kit

• Torch-TensorRT Version (e.g. 1.0.0): 2.4.0a0
• PyTorch Version (e.g. 1.0):
• CPU Architecture: aarch64
• OS (e.g., Linux): Ubuntu 22.04
• How you installed PyTorch (conda, pip, libtorch, source): nvcr.io/nvidia/pytorch:24.06-py3-igpu
• Build command you used (if compiling from source):
• Are you using local sources or building from archives: nvcr.io/nvidia/pytorch:24.06-py3-igpu
• Python version: 3.10.12
• CUDA version: 12.6.68
• GPU models and configuration:
• Any other relevant information: Jetpack 6.1 L4T 36.4.0

Additional context

Here's a screenshot for relevant comparison

Desktop:

Jetson:

[narendasan]

What version of TensorRT are you using on jetson vs x86?

[dudeperf3ct]

What version of TensorRT are you using on jetson vs x86?

On jetson I am using 24.06 PyTorch igpu image that comes with TensorRT 10.1.0.27

On dekstop, the TensorRT version 10.1.0 is used.

[dudeperf3ct]

I think this issue might be related to this PR : #3258. I am facing the same error on Jetson.

Side Note: (On Desktop) I am facing same issue as shown in this issue with GoogLeNet model: #3185

I guess not all models are supported via Torch-TensorRT library yet!

[narendasan]

@apbose can you look at this bug?

[narendasan]

Overall my inclination is that there is version mismatch somewhere since this passes on x86 and there isn't any aarch64 specific behavior in torchtrt

[narendasan]

Also you can try out the nightly or latest stable version instead of 2.4.0a0