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96d97a7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | # // Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# //
# // Licensed under the Apache License, Version 2.0 (the "License");
# // you may not use this file except in compliance with the License.
# // You may obtain a copy of the License at
# //
# // http://www.apache.org/licenses/LICENSE-2.0
# //
# // Unless required by applicable law or agreed to in writing, software
# // distributed under the License is distributed on an "AS IS" BASIS,
# // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# // See the License for the specific language governing permissions and
# // limitations under the License.
from typing import Callable, Optional
from diffusers.models.normalization import RMSNorm
from torch import nn
import torch
import torch.nn.functional as F
import numbers
from torch.nn.parameter import Parameter
from torch.nn import init
# (dim: int, eps: float, elementwise_affine: bool)
norm_layer_type = Callable[[int, float, bool], nn.Module]
class CustomLayerNorm(nn.Module):
"""
Custom LayerNorm implementation to replace Apex FusedLayerNorm
"""
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(CustomLayerNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = torch.Size(normalized_shape)
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = Parameter(torch.Tensor(*normalized_shape))
self.bias = Parameter(torch.Tensor(*normalized_shape))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
if self.elementwise_affine:
init.ones_(self.weight)
init.zeros_(self.bias)
def forward(self, input):
return F.layer_norm(
input, self.normalized_shape, self.weight, self.bias, self.eps)
class CustomRMSNorm(nn.Module):
"""
Custom RMSNorm implementation to replace Apex FusedRMSNorm
"""
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(CustomRMSNorm, self).__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = torch.Size(normalized_shape)
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = Parameter(torch.ones(*normalized_shape))
else:
self.register_parameter('weight', None)
def forward(self, input):
# RMS normalization: x / sqrt(mean(x^2) + eps) * weight
dims = tuple(range(-len(self.normalized_shape), 0))
# Calculate RMS: sqrt(mean(x^2))
variance = input.pow(2).mean(dim=dims, keepdim=True)
rms = torch.sqrt(variance + self.eps)
# Normalize
normalized = input / rms
if self.elementwise_affine:
# Convert FP8 weight to match input dtype for arithmetic operations
if hasattr(torch, 'float8_e4m3fn'):
fp8_types = (torch.float8_e4m3fn, torch.float8_e5m2)
if self.weight.dtype in fp8_types:
# Use input dtype as target (respects pipeline precision)
weight = self.weight.to(input.dtype)
return normalized * weight
return normalized * self.weight
return normalized
def get_norm_layer(norm_type: Optional[str]) -> norm_layer_type:
def _norm_layer(dim: int, eps: float, elementwise_affine: bool):
if norm_type is None:
return nn.Identity()
if norm_type == "layer":
return nn.LayerNorm(
normalized_shape=dim,
eps=eps,
elementwise_affine=elementwise_affine,
)
if norm_type == "rms":
return RMSNorm(
dim=dim,
eps=eps,
elementwise_affine=elementwise_affine,
)
if norm_type == "fusedln":
# Use custom LayerNorm instead of Apex FusedLayerNorm
return CustomLayerNorm(
normalized_shape=dim,
elementwise_affine=elementwise_affine,
eps=eps,
)
if norm_type == "fusedrms":
# Use custom RMSNorm instead of Apex FusedRMSNorm
return CustomRMSNorm(
normalized_shape=dim,
elementwise_affine=elementwise_affine,
eps=eps,
)
raise NotImplementedError(f"{norm_type} is not supported")
return _norm_layer |