In Transformer machine learning models: How is multi-head attention different from normal attention? First describe the intuition and then the mathematical perspective.
In a Transformer machine learning model, multi-head attention is a variation of the attention mechanism that allows the model to attend to different parts of the input sequence in parallel.

Intuition:
Normal attention works by computing a weighted sum of the values of the input sequence, where the weights are determined by the similarity between a query vector and each of the input vectors. Multi-head attention takes this idea one step further by allowing the model to compute multiple sets of attention weights, or heads, in parallel, each focusing on different parts of the input sequence. The outputs of each head are then concatenated and transformed linearly to produce the final attention output. By attending to multiple parts of the input sequence simultaneously, multi-head attention can potentially capture more complex relationships between different parts of the input.

Mathematical perspective:
Formally, given a set of input vectors {v_1, v_2, ..., v_n} and a query vector q, the output of multi-head attention is computed as follows:

1. First, the input vectors are linearly transformed into a set of key vectors K = {k_1, k_2, ..., k_n} and a set of value vectors V = {v_1, v_2, ..., v_n} using learned weight matrices W_k and W_v:
K_i = W_k * v_i
V_i = W_v * v_i

2. The query vector q is also transformed using a learned weight matrix W_q:
Q = W_q * q

3. For each of h attention heads, the query vector Q and key vectors K are used to compute a set of attention weights a^h = {a^h_1, a^h_2, ..., a^h_n}:
a^h_i = softmax((Q * K_i^T) / sqrt(d_k))
where d_k is the dimensionality of the key vectors, and the softmax function ensures that the attention weights sum to 1.

4. Each attention head produces a weighted sum of the value vectors V using the attention weights a^h:
h^h = sum(a^h_i * V_i)

5. The outputs of all the attention heads are concatenated into a single vector h^c, and linearly transformed using a learned weight matrix W_o to produce the final attention output:
h = W_o * h^c

By computing multiple sets of attention weights in parallel, multi-head attention allows the model to attend to different parts of the input sequence with different priorities, potentially capturing more complex relationships between different parts of the input.
How does the implementation of attention differ when used in vision transformer and in language transformers?
The implementation of attention in vision transformers and language transformers differs primarily in the type of input that the attention mechanism is applied to.

In language transformers, attention is typically applied to a sequence of word embeddings that represent a sentence or a document. The query vector is derived from the current position in the sequence, and the key and value vectors are derived from all positions in the sequence. The attention mechanism allows the model to attend to different parts of the input sequence, and has been shown to be particularly effective for tasks such as machine translation and language modeling.

In contrast, in vision transformers, attention is typically applied to a grid of patches that represent an image. The query vector is derived from each patch, and the key and value vectors are derived from all patches in the grid. The attention mechanism allows the model to attend to different regions of the image, and has been shown to be effective for tasks such as image classification and object detection.

One key difference in the implementation of attention in vision transformers is the use of positional embeddings, which provide the model with information about the spatial relationships between patches in the grid. These positional embeddings are typically added to the patch embeddings before they are fed into the attention mechanism, allowing the model to attend to both the content and the position of the patches.

Overall, while the basic idea of attention is the same in both vision transformers and language transformers, the specific implementation details differ based on the nature of the input data and the task at hand.
Is running a Vision Transformer on a smartphone an option? Could I implement it in my app or should I use a cloud based solution?