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# Copyright 2024 MIT Han Lab
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
from typing import Optional
import torch
import torch.nn as nn
from torch.nn.modules.batchnorm import _BatchNorm
from ...models.nn.triton_rms_norm import TritonRMSNorm2dFunc
from ...models.utils import build_kwargs_from_config
__all__ = ["LayerNorm2d", "TritonRMSNorm2d", "build_norm", "reset_bn", "set_norm_eps"]
class LayerNorm2d(nn.LayerNorm):
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = x - torch.mean(x, dim=1, keepdim=True)
out = out / torch.sqrt(torch.square(out).mean(dim=1, keepdim=True) + self.eps)
if self.elementwise_affine:
out = out * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)
return out
class TritonRMSNorm2d(nn.LayerNorm):
def forward(self, x: torch.Tensor) -> torch.Tensor:
return TritonRMSNorm2dFunc.apply(x, self.weight, self.bias, self.eps)
class RMSNorm2d(nn.Module):
def __init__(
self, num_features: int, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True
) -> None:
super().__init__()
self.num_features = num_features
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = torch.nn.parameter.Parameter(torch.empty(self.num_features))
if bias:
self.bias = torch.nn.parameter.Parameter(torch.empty(self.num_features))
else:
self.register_parameter("bias", None)
else:
self.register_parameter("weight", None)
self.register_parameter("bias", None)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = (x / torch.sqrt(torch.square(x.float()).mean(dim=1, keepdim=True) + self.eps)).to(x.dtype)
if self.elementwise_affine:
x = x * self.weight.view(1, -1, 1, 1) + self.bias.view(1, -1, 1, 1)
return x
# register normalization function here
REGISTERED_NORM_DICT: dict[str, type] = {
"bn2d": nn.BatchNorm2d,
"ln": nn.LayerNorm,
"ln2d": LayerNorm2d,
"trms2d": TritonRMSNorm2d,
"rms2d": RMSNorm2d,
}
def build_norm(name="bn2d", num_features=None, **kwargs) -> Optional[nn.Module]:
if name in ["ln", "ln2d", "trms2d"]:
kwargs["normalized_shape"] = num_features
else:
kwargs["num_features"] = num_features
if name in REGISTERED_NORM_DICT:
norm_cls = REGISTERED_NORM_DICT[name]
args = build_kwargs_from_config(kwargs, norm_cls)
return norm_cls(**args)
else:
return None
def reset_bn(
model: nn.Module,
data_loader: list,
sync=True,
progress_bar=False,
) -> None:
import copy
import torch.nn.functional as F
from efficientvit.apps.utils import AverageMeter, is_master, sync_tensor
from efficientvit.models.utils import get_device, list_join
from tqdm import tqdm
bn_mean = {}
bn_var = {}
tmp_model = copy.deepcopy(model)
for name, m in tmp_model.named_modules():
if isinstance(m, _BatchNorm):
bn_mean[name] = AverageMeter(is_distributed=False)
bn_var[name] = AverageMeter(is_distributed=False)
def new_forward(bn, mean_est, var_est):
def lambda_forward(x):
x = x.contiguous()
if sync:
batch_mean = x.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True) # 1, C, 1, 1
batch_mean = sync_tensor(batch_mean, reduce="cat")
batch_mean = torch.mean(batch_mean, dim=0, keepdim=True)
batch_var = (x - batch_mean) * (x - batch_mean)
batch_var = batch_var.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
batch_var = sync_tensor(batch_var, reduce="cat")
batch_var = torch.mean(batch_var, dim=0, keepdim=True)
else:
batch_mean = x.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True) # 1, C, 1, 1
batch_var = (x - batch_mean) * (x - batch_mean)
batch_var = batch_var.mean(0, keepdim=True).mean(2, keepdim=True).mean(3, keepdim=True)
batch_mean = torch.squeeze(batch_mean)
batch_var = torch.squeeze(batch_var)
mean_est.update(batch_mean.data, x.size(0))
var_est.update(batch_var.data, x.size(0))
# bn forward using calculated mean & var
_feature_dim = batch_mean.shape[0]
return F.batch_norm(
x,
batch_mean,
batch_var,
bn.weight[:_feature_dim],
bn.bias[:_feature_dim],
False,
0.0,
bn.eps,
)
return lambda_forward
m.forward = new_forward(m, bn_mean[name], bn_var[name])
# skip if there is no batch normalization layers in the network
if len(bn_mean) == 0:
return
tmp_model.eval()
with torch.no_grad():
with tqdm(total=len(data_loader), desc="reset bn", disable=not progress_bar or not is_master()) as t:
for images in data_loader:
images = images.to(get_device(tmp_model))
tmp_model(images)
t.set_postfix(
{
"bs": images.size(0),
"res": list_join(images.shape[-2:], "x"),
}
)
t.update()
for name, m in model.named_modules():
if name in bn_mean and bn_mean[name].count > 0:
feature_dim = bn_mean[name].avg.size(0)
assert isinstance(m, _BatchNorm)
m.running_mean.data[:feature_dim].copy_(bn_mean[name].avg)
m.running_var.data[:feature_dim].copy_(bn_var[name].avg)
def set_norm_eps(model: nn.Module, eps: Optional[float] = None) -> None:
for m in model.modules():
if isinstance(m, (nn.GroupNorm, nn.LayerNorm, _BatchNorm)):
if eps is not None:
m.eps = eps
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