Contents
Why is masking important in the decoder side of the transformer?
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.
How does decoder work in transformer?
The decoder is autoregressive, it begins with a start token, and it takes in a list of previous outputs as inputs, as well as the encoder outputs that contain the attention information from the input. The decoder stops decoding when it generates a token as an output.
What is Transformers mask?
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What is masked multihead attention?
It hides (masks) a part of this known output sequence for each of the parallel operations. When it executes #A – it hides (masks) the entire output.
How does self-attention work?
A self-attention module takes in n inputs, and returns n outputs. In layman’s terms, the self-attention mechanism allows the inputs to interact with each other (“self”) and find out who they should pay more attention to (“attention”). The outputs are aggregates of these interactions and attention scores.
What is Transformer attention?
In the Transformer, the Attention module repeats its computations multiple times in parallel. Each of these is called an Attention Head. The Attention module splits its Query, Key, and Value parameters N-ways and passes each split independently through a separate Head.
What is Src_key_padding_mask?
src_key_padding_mask is more like a padding marker, which masks a specific tokens in the src sequence (a.k.a. the entire column/row of the attention matrix is set to ‘-inf’).
Are Sixteen heads really better than one?
It is particularly striking that in a few layers (2, 3 and 10), some heads are sufficient, ie. it is possible to retain the same (or a better) level of performance with only one head. So yes, in some cases, sixteen heads (well, here twelve) are not necessarily better than one.
How does multihead attention work?
Multi-head Attention is a module for attention mechanisms which runs through an attention mechanism several times in parallel. The independent attention outputs are then concatenated and linearly transformed into the expected dimension.