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Turn off natural gradient update for biases.
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@ -270,26 +270,32 @@ class Cain(Optimizer):
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scale_delta)
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delta.add_(p, alpha=scale_delta)
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grad = self._change_coordinates(grad, state, forward=True)
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p_transformed = self._change_coordinates(p, state, forward=True, is_grad=False)
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# Decay the second moment running average coefficient
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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bias_correction2 = 1 - beta2 ** self._step_for_bias_correction(step)
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grad_rms = (exp_avg_sq.sqrt()).add_(eps).mul_(1.0 / bias_correction2)
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this_delta = grad / grad_rms
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smoothed_grad_rms = ng_eps * grad_rms.mean() + grad_rms
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lr_factor = (smoothed_grad_rms ** ng_pow)
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lr_factor_mean = lr_factor.mean()
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# this `lr` contains all the terms in learning rate except param_rms. Imagine
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# that param_rms is 1.0 for purposes of computing the shrinkage coefficient; actually
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# it does not matter.
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this_lr = (lr / (target_rms * lr_factor_mean)) * lr_factor
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if p.ndim() > 1:
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smoothed_grad_rms = ng_eps * grad_rms.mean() + grad_rms
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lr_factor = (smoothed_grad_rms ** ng_pow)
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lr_factor_mean = lr_factor.mean()
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# this `lr` contains all the terms in learning rate except param_rms. Imagine
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# that param_rms is 1.0 for purposes of computing the shrinkage coefficient; actually
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# it does not matter.
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this_lr = (lr / (target_rms * lr_factor_mean)) * lr_factor
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else:
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# turn off this natural gradient for biases, it seems to have
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# a bad effect, possibly because we don't decorrelate in this case, or
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# because their gradients are not dominated by noise.
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this_lr = lr / target_rms
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# change sign so it's an actual parameter change,
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# incorporate param_rms at this point.
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this_delta *= -this_lr * state["param_rms"].clamp(min=rms_eps)
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@ -323,8 +329,6 @@ class Cain(Optimizer):
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return loss
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def _change_coordinates(self,
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grad: Tensor,
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state: dict,
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