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Adding sqrt in the recurrent_forward of retnet to make it consistent with parallel_forward

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Adding sqrt in the recurrent_forward of retnet can avoid numerical underflow thus improving consistency and performance. https://github.com/microsoft/torchscale/issues/47
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wangmengzhi 2023-08-04 08:18:10 +08:00 committed by GitHub
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1 changed files with 2 additions and 2 deletions
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torchscale/component/multiscale_retention.py
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@ -105,7 +105,7 @@ class MultiScaleRetention(nn.Module):
prev_kv = incremental_state["prev_key_value"] prev_kv = incremental_state["prev_key_value"]
prev_scale = incremental_state["scale"] prev_scale = incremental_state["scale"]
scale = prev_scale * decay + 1 scale = prev_scale * decay + 1
kv = prev_kv * (1 - 1 / scale).view(self.num_heads, 1, 1) + kv / scale.view(self.num_heads, 1, 1) kv = prev_kv * (prev_scale.sqrt() * decay / scale.sqrt()).view(self.num_heads, 1, 1) + kv / scale.sqrt().view(self.num_heads, 1, 1)
# kv = prev_kv * decay.view(self.num_heads, 1, 1) + kv # kv = prev_kv * decay.view(self.num_heads, 1, 1) + kv
else: else:
scale = torch.ones_like(decay) scale = torch.ones_like(decay)