Incorrect Min NaN handling of TensorRT 10.16.1.11 when running ONNX Min on GPU

[ALinrunrun]

Description

TensorRT appears to handle NaN values incorrectly for ONNX Min.

For inputs containing NaN, ONNX Runtime propagates NaN in the output. TensorRT instead returns the non-NaN operand for mixed NaN/non-NaN inputs, which changes the result.

This appears to be a TensorRT execution issue for ONNX Min NaN semantics.

Environment

TensorRT Version: 10.16.1.11

NVIDIA GPU: N/A / not detected by nvidia-smi

NVIDIA Driver Version: N/A / nvidia-smi failed

CUDA Version: N/A / nvcc not found

CUDNN Version: N/A / torch.backends.cudnn.version() returned None

Operating System: Linux 6.17.0-20-generic x86_64, glibc 2.39

Python Version (if applicable): Python 3.11.15

Tensorflow Version (if applicable): N/A

PyTorch Version (if applicable): N/A

Baremetal or Container (if so, version): Baremetal / non-Docker environment (/proc/1/cgroup: 0::/init.scope)

Additional package versions:

ONNX Version: 1.21.0
ONNX Runtime Version: 1.25.1

Relevant Files

Model link: N/A

The ONNX model is generated inline by the minimal reproducible script below.

Steps To Reproduce

Commands or scripts:

import numpy as np
import onnx
import onnxruntime as ort
from onnx import helper, TensorProto
from _trt_helper import build_engine_from_onnx, run_engine

n = helper.make_node("Min", ["a", "b"], ["y"])
g = helper.make_graph(
    [n],
    "g",
    [
        helper.make_tensor_value_info("a", TensorProto.FLOAT, [4]),
        helper.make_tensor_value_info("b", TensorProto.FLOAT, [4]),
    ],
    [helper.make_tensor_value_info("y", TensorProto.FLOAT, [4])],
)

m = helper.make_model(g, opset_imports=[helper.make_opsetid("", 18)])
m.ir_version = 10
ob = m.SerializeToString()

a = np.array([np.nan, 12.0, np.nan, 7.5], dtype=np.float32)
b = np.array([6.0, np.nan, np.nan, 4.5], dtype=np.float32)

ort_y = ort.InferenceSession(
    ob,
    providers=["CPUExecutionProvider"],
).run(["y"], {"a": a, "b": b})[0]

eng, _ = build_engine_from_onnx(ob)
trt_y = run_engine(
    eng,
    {"a": a, "b": b},
    ["y"],
    [(4,)],
    [np.float32],
)["y"]

print("ORT:", ort_y.tolist())
print("TRT:", trt_y.tolist())

assert (np.isnan(ort_y) != np.isnan(trt_y)).any()

Have you tried the latest release?: Yes, reproduced with TensorRT 10.16.1.11.

Attach the captured .json and .bin files from TensorRT's API Capture tool if you're on an x86_64 Unix system Not attached. The issue is reproducible from the self-contained Python script above.

Can this model run on other frameworks? For example run ONNX model with ONNXRuntime (polygraphy run <model.onnx> --onnxrt): Yes. ONNX Runtime runs the same model and propagates NaN values.

Actual output:

ORT: [nan, nan, nan, 4.5]
TRT: [6.0, 12.0, nan, 4.5]

TensorRT returns 6.0 for min(nan, 6.0) and 12.0 for min(12.0, nan), while ONNX Runtime returns nan for both cases.