Hi,

I’m been trying to use the `torch.nn.functional.conv1d`

function to perform multiple convolutions on the same input in parallel using the `groups=...`

parameter - so far to no avail. Unlike other questions I’ve seen answered, I intend to perform these operations on the entire batch (i.e. not 1 kernel per 1 item in batch). I think it will be best explained in code:

```
# Data Shape
n_batch = 7
n_in_channels = 5
n_time = 17
data = torch.rand(n_batch, n_in_channels, n_time)
# Convolution shape
n_convs = 11
n_out_channels = 3
kernel_size = 13
conv_weights = torch.rand(n_convs, n_out_channels, n_in_channels, kernel_size)
bias_weights = torch.rand(n_convs, n_out_channels)
```

Noticeably, in my case one can assume that the padding, stride and dilation parameters are trivially set.

The output which I wish to compute In parallel can be computed using a loop in the following code:

```
# Out-data shape
out_data = torch.empty(n_batch, n_convs, n_out_channels, n_time)
for i in range(n_convs):
out_data[:, i, :] = torch.conv1d(data, conv_weights[i], bias_weights[i])
```

I’ve been trying to use the `groups`

parameter, but as far as I can see, it required me to copy the input data several times.

I also tried to use the `conv_transpose1d`

(at least for the special case where `kernel_size=1`

), but also without success.

Is it possible?

Thanks!