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# Copyright (c) 2025 FoundationVision
# SPDX-License-Identifier: MIT
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class SpatialGroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super(SpatialGroupNorm, self).__init__(*args, **kwargs)
def shard_norm(self, x):
dtype = x.dtype
x = x.to(torch.float32)
with torch.amp.autocast("cuda", torch.float32):
for _i in range(x.shape[0]):
x[_i:_i+1,...] = super(SpatialGroupNorm, self).forward(x[_i:_i+1,...])
x = x.to(dtype=dtype)
return x
def forward(self, x):
dtype = x.dtype
x = x.to(torch.float32)
assert x.ndim == 5
T = x.shape[2]
x = rearrange(x, "B C T H W -> (B T) C H W")
try:
x = super(SpatialGroupNorm, self).forward(x)
except:
x = self.shard_norm(x) # shard norm if OOM fallback
x = rearrange(x, "(B T) C H W -> B C T H W", T=T)
x = x.to(dtype=dtype)
return x
class Normalize(nn.Module):
def __init__(self, in_channels, norm_type, norm_axis="spatial"):
super().__init__()
self.norm_axis = norm_axis
assert norm_type in ['group', 'batch', "no"]
if norm_type == 'group':
if in_channels % 32 == 0:
self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
elif in_channels % 24 == 0:
self.norm = nn.GroupNorm(num_groups=24, num_channels=in_channels, eps=1e-6, affine=True)
else:
raise NotImplementedError
elif norm_type == 'batch':
self.norm = nn.SyncBatchNorm(in_channels, track_running_stats=False) # Runtime Error: grad inplace if set track_running_stats to True
elif norm_type == 'no':
self.norm = nn.Identity()
def _norm(self, x):
try:
x = self.norm(x)
except:
device = x.device
self.norm_cpu = self.norm.cpu()
x = self.norm_cpu(x.cpu().pin_memory()).to(device=device)
return x
def shard_norm(self, x):
dtype = x.dtype
x = x.to(torch.float32)
with torch.amp.autocast("cuda", torch.float32):
for _i in range(x.shape[0]):
x[_i:_i+1,...] = self.norm(x[_i:_i+1,...])
x = x.to(dtype=dtype)
return x
def forward(self, x):
if self.norm_axis == "spatial":
if type(x) == list:
for i in range(len(x)):
x[i] = self.norm(x[i])
return x
if x.ndim == 4:
try:
x = self.norm(x)
except:
x = self.shard_norm(x)
else:
B, C, T, H, W = x.shape
x = rearrange(x, "B C T H W -> (B T) C H W")
# x = self.shard_norm(x)
try:
x = self.norm(x)
except:
x = self.shard_norm(x)
x = rearrange(x, "(B T) C H W -> B C T H W", T=T)
elif self.norm_axis == "spatial-temporal":
x = self._norm(x)
else:
raise NotImplementedError
return x
def l2norm(t):
return F.normalize(t, dim=-1)
class LayerNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.register_buffer("beta", torch.zeros(dim))
def forward(self, x):
return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)
# https://github.com/huggingface/transformers/blob/2f12e408225b1ebceb0d2f701ce419d46678dc31/src/transformers/models/llama/modeling_llama.py#L76
class RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states, sp_slice=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
if sp_slice is None:
return (self.weight * hidden_states).to(input_dtype)
else:
return (self.weight[sp_slice] * hidden_states).to(input_dtype) # torch.float32 * torchbfloat16 in DDP will cast to torch.float32
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