Hello,

Concerning 1:
pos_embedding_layer is the lookup table for the position embeddings, it stores the weights for each position (each row
is a vector that represents the position embedding for that position). In this case since it's initialized to
context_length=4 it stores 4 positions, so for a sequence of maximum length of 4 tokens.

pos_embeddings are the position embeddings retrieved from the pos_embedding_layer.
Here it can be a bit confusing because retrieved with torch.arange(context_length) = 0,1,2,3 = 4 positions, it asks to
retrieve all the positions. But let's say you had passed torch.arange(2) it would only retrieve the first 2 positions,
in the example it would return smt like:

tensor([[ 1.7375, -0.5620, -0.6303,  ..., -0.2277,  1.5748,  1.0345],
        [ 1.6423, -0.7201,  0.2062,  ...,  0.4118,  0.1498, -0.4628],

The output is the same because in this example it's kind of the same thing, ie with .weight you directly look at all the 4 position embeddings in pos_embedding_layer.weight and with pos_embeddings tensor, you're asking for rows 0,1,2,3 (via indexing with torch.arange(max_length)), which is also every row in this case = same output.

Concerning 2:

torch.arange is used for indexing in the weight matrix of the pos_embedding_layer to get the position embeddings up to the
sequence length. For example if you had a sequence length of 2, seq_len=2, it would be torch.arange(seq_len) we retrieve pos for the first 2 tokens (like the snippet above) and not the whole max_len/context_length.

Hope that helps clear things up a bit in the meantime/until @rasbt can add more details or explain better :D

Donr do it