QConv1d: no valid convolution algorithms available in CuDNN

[xesdiny]

In https://github.com/ucbrise/actnn/blob/main/tests/test_conv_layer.py line 52-56

I just got this message when trying to run test_conv_layer.py, getting the following stacktrace:

~/code/actnn/tests$ CUDA_VISIBLE_DEVICES=1 python test_conv_layer.py
Conv1d(100, 4, kernel_size=(3,), stride=(2,), groups=2)
QConv1d(100, 4, kernel_size=(3,), stride=(2,), groups=2)
torch.Size([4, 50, 3])
torch.Size([10, 100, 2000]) tensor([2, 0, 3, 0, 0, 2, 3, 0, 2, 1], device='cuda:0')
Traceback (most recent call last):
  File "test_conv_layer.py", line 60, in <module>
    test(layer, qlayer, x, y)
  File "test_conv_layer.py", line 33, in test
    grads.append(get_grad(qlayer))
  File "test_conv_layer.py", line 27, in get_grad
    loss.backward()
  File "/data/users/root/anaconda3/envs/jukebox/lib/python3.7/site-packages/torch/tensor.py", line 245, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "/data/users/root/anaconda3/envs/jukebox/lib/python3.7/site-packages/torch/autograd/__init__.py", line 147, in backward
    allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag
  File "/data/users/root/anaconda3/envs/jukebox/lib/python3.7/site-packages/torch/autograd/function.py", line 89, in apply
    return self._forward_cls.backward(self, *args)  # type: ignore
  File "/data/users/root/code/actnn/actnn/actnn/ops.py", line 244, in backward
    return convnd.run_backward(1, ctx, grad_output, [0, 2], _single)
  File "/data/users/root/code/actnn/actnn/actnn/ops.py", line 225, in run_backward
    [ctx.needs_input_grad[0], ctx.needs_input_grad[1]])
RuntimeError: no valid convolution algorithms available in CuDNN

[xesdiny]

Is anybody there？

[merrymercy]

conv1d has bugs. We forget to delete the code.

Please do not use conv1d. You have to manually rewrite your models with Conv2D. You can think of conv1d as a special case of conv2d and rewrite your model, so this is not hard. PyTorch also did this internally for nn.Conv1d

[xesdiny]

Thx a lot~