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# Copyright 2022-2023 Xiaomi Corp. (authors: Fangjun Kuang,
# Zengwei Yao)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.
"""
This file replaces various modules in a model.
Specifically, ActivationBalancer is replaced with an identity operator;
Whiten is also replaced with an identity operator;
BasicNorm is replaced by a module with `exp` removed.
"""
import copy
from typing import List
import torch
import torch.nn as nn
from zipvoice.models.modules.scaling import (
Balancer,
Dropout3,
SwooshL,
SwooshLOnnx,
SwooshR,
SwooshROnnx,
Whiten,
)
from zipvoice.models.modules.zipformer import CompactRelPositionalEncoding
# Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule # noqa
# get_submodule was added to nn.Module at v1.9.0
def get_submodule(model, target):
if target == "":
return model
atoms: List[str] = target.split(".")
mod: torch.nn.Module = model
for item in atoms:
if not hasattr(mod, item):
raise AttributeError(
mod._get_name() + " has no " "attribute `" + item + "`"
)
mod = getattr(mod, item)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("`" + item + "` is not " "an nn.Module")
return mod
def convert_scaled_to_non_scaled(
model: nn.Module,
inplace: bool = False,
is_pnnx: bool = False,
is_onnx: bool = False,
):
"""
Args:
model:
The model to be converted.
inplace:
If True, the input model is modified inplace.
If False, the input model is copied and we modify the copied version.
is_pnnx:
True if we are going to export the model for PNNX.
is_onnx:
True if we are going to export the model for ONNX.
Return:
Return a model without scaled layers.
"""
if not inplace:
model = copy.deepcopy(model)
d = {}
for name, m in model.named_modules():
if isinstance(m, (Balancer, Dropout3, Whiten)):
d[name] = nn.Identity()
elif is_onnx and isinstance(m, SwooshR):
d[name] = SwooshROnnx()
elif is_onnx and isinstance(m, SwooshL):
d[name] = SwooshLOnnx()
elif is_onnx and isinstance(m, CompactRelPositionalEncoding):
# We want to recreate the positional encoding vector when
# the input changes, so we have to use torch.jit.script()
# to replace torch.jit.trace()
d[name] = torch.jit.script(m)
for k, v in d.items():
if "." in k:
parent, child = k.rsplit(".", maxsplit=1)
setattr(get_submodule(model, parent), child, v)
else:
setattr(model, k, v)
return model
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