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[ennhance]: use fused pytorch ops #297

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[ennhance]: use fused pytorch ops #297

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18 changes: 9 additions & 9 deletions RWKV-v5/src/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -855,12 +855,12 @@ def forward(self, x, v_first):
H = self.n_head
xx = self.time_shift(x) - x

xr = x + xx * self.x_r
xw = x + xx * self.x_w
xk = x + xx * self.x_k
xv = x + xx * self.x_v
xa = x + xx * self.x_a
xg = x + xx * self.x_g
xr = torch.addcmul(x, xx, self.x_r)
xw = torch.addcmul(x, xx, self.x_w)
xk = torch.addcmul(x, xx, self.x_k)
xv = torch.addcmul(x, xx, self.x_v)
xa = torch.addcmul(x, xx, self.x_a)
xg = torch.addcmul(x, xx, self.x_g)

r = self.receptance(xr)
w = -F.softplus(-(self.w0 + torch.tanh(xw @ self.w1) @ self.w2)) - 0.5 # soft-clamp to (-inf, -0.5)
Expand All @@ -869,13 +869,13 @@ def forward(self, x, v_first):
if self.layer_id == 0:
v_first = v # store the v of the first layer
else:
v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2) # add value residual
v = torch.lerp(v, v_first, torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2)) # add value residual
a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2) # a is "in-context learning rate"
g = torch.sigmoid(xg @ self.g1) @ self.g2

kk = k * self.k_k
kk = F.normalize(kk.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,C)
k = k * (1 + (a-1) * self.k_a)
k = k.addcmul(k * (a - 1), self.k_a)

x = RUN_CUDA_RWKV7g(r, w, k, v, -kk, kk*a)
x = self.ln_x(x.view(B * T, C)).view(B, T, C)
Expand Down Expand Up @@ -970,7 +970,7 @@ def __init__(self, args, layer_id):
def forward(self, x):
xx = self.time_shift(x) - x

k = x + xx * self.x_k
k = torch.addcmul(x, xx, self.x_k)
k = torch.relu(self.key(k)) ** 2

return self.value(k)
Expand Down