What is masking in Transformers?
Masking is needed to prevent the attention mechanism of a transformer from “cheating” in the decoder when training (on a translating task for instance). This kind of “ cheating-proof masking” is not present in the encoder side.
Why do we require look ahead mask in Transformers?
Look-ahead Mask: While generating target sequences at the decoder, since the Transformer uses self-attention, it tends to include all the words from the decoder inputs. Only the words preceding the current word may contribute to the generation of the next word. Masked Multi-Head Attention ensures this.
What is key padding mask?
The key_padding_mask is used to mask out positions that are padding, i.e., after the end of the input sequence. This is always specific to the input batch and depends on how long are the sequence in the batch compared to the longest one. It is a 2D tensor of shape batch size × input length.
What is the main idea of transformer models?
A transformer is a deep learning model that adopts the mechanism of attention, differentially weighing the significance of each part of the input data. It is used primarily in the field of natural language processing (NLP) and in computer vision (CV).
What is mask in PyTorch?
PyTorch and NumPy allow setting certain elements of a tensor using boolean masks. Mask are the same size as the tensor being masked and only those elements are updated where the mask value is true: X = torch.
When to use a mask in the transformer model?
Concerning the transformer model, a mask is used to mask out attention scores (replace with 1e-9) prior to the matrix multiplication with the value tensor. Regarding the masking, I have 3 short questions and would appreciate if you could clarify those:
Why is masking important in the transformer in PyTorch?
This means the original meaning in the embedding vector won’t be lost when we add them together. Masking plays an important role in the transformer. It serves two purposes: In the encoder and decoder: To zero attention outputs wherever there is just padding in the input sentences.
What is the first input to the decoder in a transformer model?
At each decoding time step, the decoder receives 2 inputs: the encoder output: this is computed once and is fed to all layers of the decoder at each decoding time step as key (K e n d e c) and value (V e n d e c) for the encoder-decoder attention blocks.
How does a Transformers work step by step?
On a high level, the encoder maps an input sequence into an abstract continuous representation that holds all the learned information of that input. The decoder then takes that continuous representation and step by step generates a single output while also being fed the previous output.