关于Transformer， QKV的意义表示其更像是一个可学习的查询系统，或许以前搜索引擎的算法就与此有关或者某个分支的搜索算法与此类似。
Can anyone help me to understand this image? - #2 by J_Johnson - nlp - PyTorch Forums
Embeddings - these are learnable weights where each token(token could be a word, sentence piece, subword, character, etc) are converted into a vector, say, with 500 values between 0 and 1 that are trainable.
Positional Encoding - for each token, we want to inform the model where it’s located, orderwise. This is because linear layers are not ideal for handling sequential information. So we manually pass this in by adding a vector of sine and cosine values on the first 2 elements in the embedding vector.
This sequence of vectors goes through an attention layer, which basically is like a learnable digitized database search function with keys, queries and values. In this case, we are “searching” for the most likely next token.
The Feed Forward is just a basic linear layer, but is applied across each embedding in the sequence separately(i.e. 3 dim tensor instead of 2 dim).
Then the final Linear layer is where we want to get out our predicted next token in the form of a vector of probabilities, which we apply a softmax to put the values in the range of 0 to 1.
There are two sides because when that diagram was developed, it was being used in language translations. But generative language models for next token prediction just use the Transformer decoder and not the encoder.
Here is a PyTorch tutorial that might help you go through how it works.
Language Modeling with nn.Transformer and torchtext — PyTorch Tutorials 2.0.1+cu117 documentation
