| import torch | |
| from einops import rearrange | |
| from torch import Tensor | |
| def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor: | |
| if pe is not None: | |
| q, k = apply_rope(q, k, pe) | |
| x = torch.nn.functional.scaled_dot_product_attention(q, k, v) | |
| x = rearrange(x, "B H L D -> B L (H D)") | |
| return x | |
| def rope(pos: Tensor, dim: int, theta: int) -> Tensor: | |
| assert dim % 2 == 0 | |
| scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim | |
| omega = 1.0 / (theta**scale) | |
| out = torch.einsum("...n,d->...nd", pos, omega) | |
| out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) | |
| out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) | |
| return out.float() | |
| def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: | |
| xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) | |
| xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) | |
| xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] | |
| xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] | |
| return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) | |