Method normalize

deepspeed/runtime/eigenvalue.py:50–55

(self, v)

            return torch.from_numpy(x).to(device)

    def normalize(self, v):
        norm_squared = self.inner_product(v, v)
        norm = norm_squared**0.5 + self.stability
        normalized_vectors = [vector / norm for vector in v]
        normalized_vectors = [self.nan_to_num(vector) for vector in normalized_vectors]
        return normalized_vectors

    def inner_product(self, xs, ys):
        return sum([torch.sum(x * y) for (x, y) in zip(xs, ys)])