Converting the model to onnx introduces errors

[geekchen007]

We found that there is an error between the ONNX model and the pytorch(pth), with a cosine distance of approximately 5%

open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_e16', cache_dir)
reference:
https://github.com/LAION-AI/CLIP_benchmark/blob/main/clip_benchmark/models/open_clip.py

https://clip-as-service.s3.us-east-2.amazonaws.com/models-436c69702d61732d53657276696365/onnx/ViT-B-32-laion2b_e16/visual.onnx

[ZiniuYu]

Hi @geekchen007,

Thank you for bringing this issue to our attention. To better understand the problem, could you please let us know what device you are using to run the model? Our pytorch model uses mixed-precision, which means it utilizes both float32 and float16. However, we only use float16 on the GPU, which may result in some precision loss.

Looking forward to hearing back from you.

[geekchen007]

torch==1.13.1
onnx==1.13.1
onnxruntime===1.11.1

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2
| 0 Tesla V100-SXM2

[ZiniuYu]

Sorry there is something not clear in my previous response. What I meant is that we use fp16 with onnx on gpu (which you may observe the difference), and fp32 with onnx on cpu.

Are you running in cpu mode or gpu mode? Could you please share the scripts/data you found the discrepancy?