Incorrect GroupNormalization result of TensorRT 10.16.1.11 when running ONNX GroupNormalization(num_groups=1) on GPU

## Description TensorRT appears to handle ONNX `GroupNormalization` with `num_groups=1` incorrectly. For `GroupNormalization(num_groups=1)`, the normalization should be computed across all channels and spatial dimensions in each sample, matching PyTorch `torch.nn.functional.group_norm(..., num_groups=1)` and ONNX Runtime. However, TensorRT produces an output that matches per-channel instance normalization instead. In other words, TensorRT seems to normalize each channel independently, as if `num_groups=C`, rather than treating all channels as one group. The same ONNX model runs correctly in ONNX Runtime and matches PyTorch. ## 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): 2.11.0+cpu 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 ``` import numpy as np import onnx from onnx import helper, TensorProto import torch import onnxruntime as ort from _trt_helper import build_engine_from_onnx, run_engine C, H, W = 8, 4, 4 X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, C, H, W]) Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, C, H, W]) scale = helper.make_tensor("scale", TensorProto.FLOAT, [C], np.ones(C, np.float32)) bias = helper.make_tensor("bias", TensorProto.FLOAT, [C], np.zeros(C, np.float32)) node = helper.make_node( "GroupNormalization", ["X", "scale", "bias"], ["Y"], num_groups=1, epsilon=1e-5, ) g = helper.make_graph([node], "g", [X], [Y], initializer=[scale, bias]) m = helper.make_model(g, opset_imports=[helper.make_opsetid("", 21)]) m.ir_version = 10 onnx.checker.check_model(m) onnx_bytes = m.SerializeToString() x = np.random.default_rng(0).standard_normal((1, C, H, W)).astype(np.float32) eng, _ = build_engine_from_onnx(onnx_bytes, fp16=False) trt_out = run_engine( eng, {"X": x}, ["Y"], [(1, C, H, W)], [np.float32], )["Y"] torch_out = torch.nn.functional.group_norm( torch.from_numpy(x), 1, weight=torch.ones(C), bias=torch.zeros(C), eps=1e-5, ).numpy() ort_out = ort.InferenceSession( onnx_bytes, providers=["CPUExecutionProvider"], ).run(["Y"], {"X": x})[0] instance_out = np.zeros_like(x) for c in range(C): instance_out[0, c] = ( x[0, c] - x[0, c].mean() ) / np.sqrt(x[0, c].var() + 1e-5) print("TRT[0,0,0,:4]: ", trt_out[0, 0, 0, :4]) print("torch[0,0,0,:4]: ", torch_out[0, 0, 0, :4]) print("ORT[0,0,0,:4]: ", ort_out[0, 0, 0, :4]) print("instance[0,0,0,:4]:", instance_out[0, 0, 0, :4]) print("max|TRT - torch|:", float(np.max(np.abs(trt_out - torch_out)))) print("max|TRT - ORT|: ", float(np.max(np.abs(trt_out - ort_out)))) print("max|TRT - instance|:", float(np.max(np.abs(trt_out - instance_out)))) assert np.max(np.abs(trt_out - torch_out)) > 1e-2 assert np.max(np.abs(trt_out - instance_out)) < 1e-4 ``` **Commands or scripts**: **Have you tried [the latest release](https://developer.nvidia.com/tensorrt)?**: Yes, reproduced with TensorRT 10.16.1.11. **Attach the captured .json and .bin files from [TensorRT's API Capture tool](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/capture-replay.html) 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 and PyTorch agree with each other. TensorRT differs from both and instead matches per-channel instance normalization. ### Actual output: ``` TRT[0,0,0,:4]: [0.4456725 0.15238671 1.031132 0.4219784 ] torch[0,0,0,:4]: [ 0.06682756 -0.2042495 0.6079537 0.04492765] ORT[0,0,0,:4]: [ 0.06682757 -0.20424952 0.6079538 0.04492767] instance[0,0,0,:4]: [0.4456726 0.15238675 1.0311322 0.4219785 ] max|TRT - torch|: 0.6626157760620117 max|TRT - ORT|: 0.6626157760620117 max|TRT - instance|: 2.384185791015625e-07 ``` This suggests TensorRT is treating GroupNormalization(num_groups=1) like instance normalization instead of normalizing over the single group containing all channels.