Forums - qnn2.10.0 conv1d with group parameter, executed in fp16 precision on the SA8295 htp backend, resulted in an error.

With this model, the problem can be perfectly reproduced. Kernel size is 21, and the input is a tensor filled with all ones.

version: v2.10.0.230425122932_54038

import torch

import torch.nn as nn

import numpy as np

 

torch.backends.cudnn.enabled = False

 

class test_conv1d(nn.Module):

    def __init__(self):

        super().__init__()

        self.conv1d = nn.Conv1d(256, 256, 21,padding=10,groups=256,bias=False)

        

    def forward(self,feature):

        out = self.conv1d(feature)

        return out 

 

 

torch.set_printoptions(threshold=np.inf,sci_mode=False,linewidth=1000000)

 

 

model = test_conv1d()

model.to(torch.device('cpu'))

model.eval()

 

feature = torch.ones(1,256,40)

out = model(feature)

print('input:',feature)

print('out shape:',out.shape)

print('out value:',out)

 

scripted_model = torch.jit.trace(model,(feature))

scripted_model.save("test_conv1d.pt")