57 lines
1.5 KiB
Rust
57 lines
1.5 KiB
Rust
use burn::tensor::{activation::softmax, backend::Backend, Tensor};
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use std::f32::NEG_INFINITY;
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pub fn qkv_attention<B: Backend>(
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q: Tensor<B, 3>,
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k: Tensor<B, 3>,
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v: Tensor<B, 3>,
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mask: Option<Tensor<B, 2>>,
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n_head: usize,
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) -> Tensor<B, 3> {
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let [n_batch, n_qctx, n_state] = q.dims();
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let [_, n_ctx, _] = k.dims();
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let scale = (n_state as f64 / n_head as f64).powf(-0.25);
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let n_hstate = n_state / n_head;
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let q = q
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.reshape([n_batch, n_qctx, n_head, n_hstate])
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.swap_dims(1, 2)
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* scale;
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let k = k
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.reshape([n_batch, n_ctx, n_head, n_hstate])
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.swap_dims(1, 2)
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.transpose()
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* scale;
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let v = v
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.reshape([n_batch, n_ctx, n_head, n_hstate])
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.swap_dims(1, 2);
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let qk = q.matmul(k);
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// apply mask
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let qk = if let Some(mask) = mask {
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qk + mask.slice([0..n_qctx, 0..n_ctx]).unsqueeze::<4>()
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} else {
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qk
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};
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// normalize value weightings
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let w = softmax(qk, 3);
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let o = w.matmul(v).swap_dims(1, 2).flatten(2, 3);
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return o;
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}
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pub fn attn_decoder_mask<B: Backend>(seq_length: usize, device: &B::Device) -> Tensor<B, 2> {
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let mut mask = Tensor::<B, 2>::zeros([seq_length, seq_length], device);
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for i in 0..(seq_length - 1) {
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let values = Tensor::<B, 2>::zeros([1, seq_length - (i + 1)], device).add_scalar(NEG_INFINITY);
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mask = mask.slice_assign([i..i + 1, i + 1..seq_length], values);
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}
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return mask;
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}
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