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import torch
import math
def gdpa_attn(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor, GQ: torch.Tensor, GK: torch.Tensor) -> torch.Tensor:
"""
Baseline GDPA 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
GQ: Input tensor of shape (Z, H, M, Dq) - query gate tensor
GK: Input tensor of shape (Z, H, N, Dq) - key gate tensor
Returns:
Output tensor of shape (Z, H, M, Dv) - attention output
"""
scale = 1.0 / math.sqrt(Q.shape[-1])
Qg = Q * torch.sigmoid(GQ)
Kg = K * torch.sigmoid(GK)
scores = torch.matmul(Qg, Kg.transpose(-1, -2)) * scale
P = torch.softmax(scores, dim=-1)
O = torch.matmul(P, V).to(torch.float16)
return O
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