After upgrading from 8.6 to 10.8 or 10.9, tensorrt's results are inconsistent with onnxrt

[2730gf]

Description

After upgrading tensorrt to 10.8, the model accuracy decreased.
After setting all nodes of the model to output, the model accuracy was aligned. It was suspected that the fusion strategy introduced by the upgrade caused the accuracy problem.
Finally, we used the polygraphy tool to find an onnx subgraph that could reproduce the problem.
By the way, it needs to be reminded that the model only uses fp32 precision, not even fp16 or int8

Environment

TensorRT Version: 10.8 & 10.9

NVIDIA GPU: 3090

NVIDIA Driver Version: 550.67

CUDA Version: 12.2

Relevant Files

I post onnx file here:
Model link: https://github.com/2730gf/issues/blob/main/trt_inconsistent/mini_graph.onnx

Steps To Reproduce

Commands or scripts:
polygraphy run mini_graph.onnx -v -v -v -v -v --pool-limit workspace:20G --onnxrt --trt --validate --atol 1e-4 --rtol 1e-3 --onnx-outputs p2o.Concat.125 --trt-outputs p2o.Concat.125

[lix19937]

@2730gf Your output distribution is very similar， can you upload the full screenshot ?

[2730gf]

@lix19937 Thanks for your reply. I have updated the screenshot.

[lix19937]

So from your sshot, the max dist in one place, and mainly dist in [0, 0.0316], different arch of gpu, the tactic of kernel impl is diffenrent, BTW, you can evaluate through indicators.

[2730gf]

@lix19937 This model is fp32, and generally speaking, using fp32 inference will not produce such a large diff.
And when we using trt8.6, the accuracy is completely aligned, but trt10.8 is not aligned.

Most importantly, it has already affected the accuracy of the model. That's why we start to analyze the output diff of the intermediate nodes

[lix19937]

Try to open --noTF32 , then re-compare.

BTW, you can use polygraphy to location which layer begin to diff.

[2730gf]

@lix19937
Under polygraphy, TF32 is turned off by default.
[I] TF32 is disabled by default. Turn on TF32 for better performance with minor accuracy differences.
Initially, we discovered an accuracy issue in a large-scale model. Currently, the mini_graph.onnx serves as a minimal reproducible example of the problem, containing only dozens of nodes.
We observed that when all intermediate nodes are designated as outputs, the accuracy aligns. Therefore, we suspect that the new kernel fusion is responsible for the issue.
These kernel fusion is not transparent to users. I hope NV can help take a look.

[lix19937]

Sorry, I thought you were using trtexec.
From your onnx, it like a self-atten, can you tell me the torch implementation corresponding to this subgraph. From historical experience , compiling an atten layer which uses torch's scaled_dot_product_attention to a trt engine results in incorrect outputs.
https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py#L159

[2730gf]

@lix19937 Hello, it is not trained using torch, but it is indeed a transformer-based model. Is there any way to fix this accuracy issue?