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To optimize the runtime of this program, we can leverage some of PyTorch's functions for better performance. Specifically, we can use `torch.rsqrt` and `torch.mean` wisely to optimize the normalization calculation. This can be beneficial from a performance perspective since certain operations might be optimized internally. Here is an optimized version of the code. ### Explanation. - `torch.mean(x * x, dim=self.dim, keepdim=True)`: Calculating the mean of the squared values directly. - `torch.rsqrt(mean_square)`: Using `torch.rsqrt` to compute the reciprocal of the square root. This can be more efficient than computing the square root and then taking the reciprocal separately. - `x * torch.rsqrt(mean_square)`: Multiplying `x` by the reciprocal square root we computed above. This reformulation can lead to improved performance because it reduces the number of operations by specifically leveraging PyTorch's optimized backend operations. |
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| .. | ||
| audio | ||
| aura | ||
| cascade | ||
| cosmos | ||
| flux | ||
| genmo | ||
| hunyuan3d | ||
| hunyuan_video | ||
| hydit | ||
| lightricks | ||
| lumina | ||
| models | ||
| modules | ||
| pixart | ||
| wan | ||
| common_dit.py | ||
| util.py | ||