Number of multiplications per token in a GPT model

The following is for a "classic" GPT-2-style model, the following estimates the number attention multiplications.

For each layer (L):

for each attention head (h):
- K = d_model * d_head (takes embedding of one token and converts to vector of length d_head)
- Q = d_model * d_head (same)
- K Q dot product for attention pattern: n_ctx * d_head (n_ctx times dot products of vectors of size d_head, once new K vs every Q. Q vs every K zeroed out by causality.)
- new value vector for new token: d_model * d_model
- new updates: n_ctx * d_model (multiply each value vector by the new attention column scalar)
fully connected: d_model * d_ff + d_ff * d_model (converts the embedding to the hidden layer size and then back)

So the total sum is:

L * (
  h * (
    2 * d_model * d_head +
    n_ctx * d_head +
    d_model * d_model +
    n_ctx * d_model
  ) +
  2 * d_model * d_ff
)

This is coded at: llm_count_mults.py.

Bibliography:

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