| import torch | |
| import math | |
| def decoding_attn(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor) -> torch.Tensor: | |
| """ | |
| Baseline decoding attention implementation using PyTorch. | |
| Args: | |
| Q: Input tensor of shape (Z, H, M, Dq) - query tensor | |
| K: Input tensor of shape (Z, H, N, Dq) - key tensor | |
| V: Input tensor of shape (Z, H, N, Dv) - value tensor | |
| Returns: | |
| Output tensor of shape (Z, H, M, Dv) - attention output | |
| """ | |
| # Q:[Z,H,M,D], K:[Z,H,N,D], V:[Z,H,N,Dv] | |
| Z, H, M, D = Q.shape | |
| N = K.shape[-2] | |
| scale = 1.0 / math.sqrt(D) | |
| scores = torch.matmul(Q, K.transpose(-1, -2)) * scale # [Z,H,M,N] | |
| P = torch.softmax(scores, dim=-1) | |
| O = torch.matmul(P, V).to(torch.float16) | |
| return O | |