This seems to be one of the common questions on here (1, 2, 3), but I am still struggling to define the right shape for input to PyTorch conv1D.

I have text sequences of length 512 (number of tokens per sequence) with each token being represented by a vector of length 768 (embedding). The batch size I am using is 6.

So my input tensor to conv1D is [6, 512, 768].

```
input = torch.randn(6, 512, 768)
```

Now, I want to convolve over the length of my sequence (512) with a kernel size of 2 using the conv1D layer from PyTorch.

I assumed that â€śin_channelsâ€ť are the embedding dimension of the conv1D layer. If so, then a conv1D layer will be defined in this way where

in_channels = embedding dimension (768)

out_channels = 100 (arbitrary number)

kernel = 2

```
convolution_layer = nn.conv1D(768, 100, 2)
feature_map = convolution_layer(input)
```

But with this assumption, I get the following error:

```
RuntimeError: Given groups=1, weight of size 100 768 2, expected input `[4, 512, 768]` to have 768 channels, but got 512 channels instead
```

Then I assumed that â€śin_channelsâ€ť is the sequence length of the input sequence. If so, then a conv1D layer will be defined in this way where

in_channels = sequence length (512)

out_channels = 100 (arbitrary number)

kernel = 2

```
convolution_layer = nn.conv1D(512, 100, 2)
feature_map = convolution_layer(input)
```

This works fine and I get an output feature map of dimension `[batch_size, 100, 767]`

. However, I am confused. Shouldnâ€™t the convolutional layer convolve over the sequence length of 512 and output a feature map of dimension `[batch_size, 100, 511]`

?

I will be really grateful for your help.