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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
import gc
from copy import deepcopy
from typing import Union
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.compile(fullgraph=True)
def fused_rms_norm(x: torch.Tensor, weight: nn.Parameter, eps: float):
x = x.float()
return (x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True).add_(eps))) * weight
@torch.compile(fullgraph=True)
def fused_ada_layer_norm(C: int, eps: float, x: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor):
x = x.float()
x = F.layer_norm(input=x, normalized_shape=(C,), weight=None, bias=None, eps=eps)
return x.mul(scale.add(1)).add_(shift)
@torch.compile(fullgraph=True)
def fused_ada_rms_norm(C: int, eps: float, x: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor):
x = x.float()
x = (x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True).add_(eps)))
return x.mul(scale.add(1)).add_(shift)