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113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file replaces various modules in a model.
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Specifically, ActivationBalancer is replaced with an identity operator;
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Whiten is also replaced with an identity operator;
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BasicNorm is replaced by a module with `exp` removed.
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"""
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import copy
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from typing import List
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import torch
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import torch.nn as nn
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from scaling import ActivationBalancer, BasicNorm, Whiten
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class NonScaledNorm(nn.Module):
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"""See BasicNorm for doc"""
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def __init__(
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self,
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num_channels: int,
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eps_exp: float,
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channel_dim: int = -1, # CAUTION: see documentation.
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):
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super().__init__()
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self.num_channels = num_channels
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self.channel_dim = channel_dim
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self.eps_exp = eps_exp
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if not torch.jit.is_tracing():
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assert x.shape[self.channel_dim] == self.num_channels
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scales = (
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torch.mean(x * x, dim=self.channel_dim, keepdim=True) + self.eps_exp
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).pow(-0.5)
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return x * scales
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def convert_basic_norm(basic_norm: BasicNorm) -> NonScaledNorm:
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assert isinstance(basic_norm, BasicNorm), type(BasicNorm)
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norm = NonScaledNorm(
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num_channels=basic_norm.num_channels,
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eps_exp=basic_norm.eps.data.exp().item(),
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channel_dim=basic_norm.channel_dim,
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)
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return norm
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# Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule # noqa
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# get_submodule was added to nn.Module at v1.9.0
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def get_submodule(model, target):
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if target == "":
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return model
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atoms: List[str] = target.split(".")
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mod: torch.nn.Module = model
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for item in atoms:
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if not hasattr(mod, item):
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raise AttributeError(mod._get_name() + " has no attribute `" + item + "`")
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mod = getattr(mod, item)
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if not isinstance(mod, torch.nn.Module):
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raise AttributeError("`" + item + "` is not an nn.Module")
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return mod
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def convert_scaled_to_non_scaled(
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model: nn.Module,
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inplace: bool = False,
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):
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"""
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Args:
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model:
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The model to be converted.
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inplace:
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If True, the input model is modified inplace.
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If False, the input model is copied and we modify the copied version.
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Return:
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Return a model without scaled layers.
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"""
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if not inplace:
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model = copy.deepcopy(model)
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d = {}
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for name, m in model.named_modules():
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if isinstance(m, BasicNorm):
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d[name] = convert_basic_norm(m)
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elif isinstance(m, (ActivationBalancer, Whiten)):
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d[name] = nn.Identity()
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for k, v in d.items():
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if "." in k:
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parent, child = k.rsplit(".", maxsplit=1)
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setattr(get_submodule(model, parent), child, v)
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else:
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setattr(model, k, v)
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return model
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