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# -*- coding: utf-8 -*-
from typing import Optional
import torch
def ceildiv(a, b):
return -(a // -b)
def naive_recurrent_gla(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
gk: torch.Tensor,
initial_state: Optional[torch.Tensor] = None,
output_final_state: bool = False
):
dtype = q.dtype
q, k, v, gk = map(lambda x: x.float(), (q, k, v, gk))
B, H, T, K, V = *q.shape, v.shape[-1]
o = torch.zeros_like(v)
scale = K ** -0.5
h = q.new_zeros(B, H, K, V, dtype=torch.float32)
if initial_state is not None:
h += initial_state.float()
for i in range(T):
q_i = q[:, :, i] * scale
k_i = k[:, :, i]
v_i = v[:, :, i]
gk_i = gk[:, :, i].exp()
kv_i = k_i[..., None] * v_i[..., None, :]
h = h * gk_i[..., None] + kv_i
o[:, :, i] = (q_i[..., None] * h).sum(-2)
if not output_final_state:
h = None
return o.to(dtype), h
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