nn.Conv1d will use dim2 as the time axis and convolve (or rather cross-correlate) in this dimension.
Therefore, you would need to permute the dimensions in your input.
Here is a small example:
conv = nn.Conv1d(6, 1, kernel_size=5, padding=2)
input = torch.randn(6, 6, 1)
input = input.permute(2, 1, 0)
print(input.shape) # [batch_size, channels, seq]
> torch.Size([1, 6, 6])
output = conv(input)