two forms of positional embedding return

Let me explain the difference between these two indexing operations in a positional embedding context:

1. self.pe[:, :x.size(1)]:

   • This selects ALL rows (first :)
   • But only the first x.size(1) columns
   • Typically used when you want to slice the positional embedding to match the sequence length of your input tensor
   • Assumes self.pe has shape (embedding_dim, max_sequence_length)
   • Useful in scenarios where you have a fixed positional embedding matrix and want to extract embeddings for the current input's sequence length

2. self.pe[:x.size(0), :]:

   • This selects the first x.size(0) rows
   • But ALL columns (second :)
   • Typically used when you want to slice the positional embedding to match the batch size of your input tensor
   • Assumes self.pe has shape (max_batch_size, embedding_dim)
   • Useful in scenarios where you have a fixed positional embedding matrix and want to extract embeddings for the current batch size

The key differences are:

• They slice different dimensions of the positional embedding matrix
• They are used in different contexts depending on the shape of your positional embedding matrix and how you want to extract embeddings

Here's a quick example to illustrate:

import torch

# Scenario 1: PE with shape (embedding_dim, max_seq_length)
# Slicing to match sequence length
pe1 = torch.randn(512, 100)  # 512 embedding dim, max 100 sequence length
x1 = torch.randn(32, 50, 512)  # batch_size=32, seq_length=50, embedding_dim=512
sliced_pe1 = pe1[:, :x1.size(1)]  # Will be (512, 50)

# Scenario 2: PE with shape (max_batch_size, embedding_dim)
# Slicing to match batch size
pe2 = torch.randn(64, 512)  # max 64 batch size, 512 embedding dim
x2 = torch.randn(32, 100, 512)  # batch_size=32, seq_length=100, embedding_dim=512
sliced_pe2 = pe2[:x2.size(0), :]  # Will be (32, 512)

The choice between these two depends on:

1. The shape of your positional embedding matrix
2. How you want to extract positional embeddings relative to your input tensor
3. The specific implementation of your positional embedding strategy