Hi, reproducing it into 30 lines of code is quite challenging, I’m trying to isolate the specific part causing this ‘slow forward’ but at the moment I didn’t had success.
Here you can see a detailed profiling of all ops done during forward (https://pastebin.com/RYRFFruE 5).
The model I’m using is the ‘DGCNN’ from https://github.com/WangYueFt/dgcnn/blob/master/pytorch/model.py 1 (Wang et Al.), in the forward a nearest neighboors graph is constructed at each layer. Still trying to understand if the problem is in layers forward or in some of the ops used for graph construction.
Thank you
Edit:
If I exclude the graph construction part (using random tensor) forward performances seems to be the comparable between 1.2 and 1.5. At the same time, by profiling the function used for graph construction between the two versions I’m not experiencing very different behaviours… I’m confused