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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
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