mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-14 10:57:37 +01:00
149 lines
4.3 KiB
Python
149 lines
4.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
"""
|
|
Utilities to save and load models.
|
|
"""
|
|
from contextlib import contextmanager
|
|
|
|
import functools
|
|
import hashlib
|
|
import inspect
|
|
import io
|
|
from pathlib import Path
|
|
import warnings
|
|
|
|
from omegaconf import OmegaConf
|
|
from diffq import DiffQuantizer, UniformQuantizer, restore_quantized_state
|
|
import torch
|
|
|
|
|
|
def get_quantizer(model, args, optimizer=None):
|
|
"""Return the quantizer given the XP quantization args."""
|
|
quantizer = None
|
|
if args.diffq:
|
|
quantizer = DiffQuantizer(
|
|
model, min_size=args.min_size, group_size=args.group_size)
|
|
if optimizer is not None:
|
|
quantizer.setup_optimizer(optimizer)
|
|
elif args.qat:
|
|
quantizer = UniformQuantizer(
|
|
model, bits=args.qat, min_size=args.min_size)
|
|
return quantizer
|
|
|
|
|
|
def load_model(path_or_package, strict=False):
|
|
"""Load a model from the given serialized model, either given as a dict (already loaded)
|
|
or a path to a file on disk."""
|
|
if isinstance(path_or_package, dict):
|
|
package = path_or_package
|
|
elif isinstance(path_or_package, (str, Path)):
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore")
|
|
path = path_or_package
|
|
package = torch.load(path, 'cpu')
|
|
else:
|
|
raise ValueError(f"Invalid type for {path_or_package}.")
|
|
|
|
klass = package["klass"]
|
|
args = package["args"]
|
|
kwargs = package["kwargs"]
|
|
|
|
if strict:
|
|
model = klass(*args, **kwargs)
|
|
else:
|
|
sig = inspect.signature(klass)
|
|
for key in list(kwargs):
|
|
if key not in sig.parameters:
|
|
warnings.warn("Dropping inexistant parameter " + key)
|
|
del kwargs[key]
|
|
model = klass(*args, **kwargs)
|
|
|
|
state = package["state"]
|
|
|
|
set_state(model, state)
|
|
return model
|
|
|
|
|
|
def get_state(model, quantizer, half=False):
|
|
"""Get the state from a model, potentially with quantization applied.
|
|
If `half` is True, model are stored as half precision, which shouldn't impact performance
|
|
but half the state size."""
|
|
if quantizer is None:
|
|
dtype = torch.half if half else None
|
|
state = {k: p.data.to(device='cpu', dtype=dtype) for k, p in model.state_dict().items()}
|
|
else:
|
|
state = quantizer.get_quantized_state()
|
|
state['__quantized'] = True
|
|
return state
|
|
|
|
|
|
def set_state(model, state, quantizer=None):
|
|
"""Set the state on a given model."""
|
|
if state.get('__quantized'):
|
|
if quantizer is not None:
|
|
quantizer.restore_quantized_state(model, state['quantized'])
|
|
else:
|
|
restore_quantized_state(model, state)
|
|
else:
|
|
model.load_state_dict(state)
|
|
return state
|
|
|
|
|
|
def save_with_checksum(content, path):
|
|
"""Save the given value on disk, along with a sha256 hash.
|
|
Should be used with the output of either `serialize_model` or `get_state`."""
|
|
buf = io.BytesIO()
|
|
torch.save(content, buf)
|
|
sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8]
|
|
|
|
path = path.parent / (path.stem + "-" + sig + path.suffix)
|
|
path.write_bytes(buf.getvalue())
|
|
|
|
|
|
def serialize_model(model, training_args, quantizer=None, half=True):
|
|
args, kwargs = model._init_args_kwargs
|
|
klass = model.__class__
|
|
|
|
state = get_state(model, quantizer, half)
|
|
return {
|
|
'klass': klass,
|
|
'args': args,
|
|
'kwargs': kwargs,
|
|
'state': state,
|
|
'training_args': OmegaConf.to_container(training_args, resolve=True),
|
|
}
|
|
|
|
|
|
def copy_state(state):
|
|
return {k: v.cpu().clone() for k, v in state.items()}
|
|
|
|
|
|
@contextmanager
|
|
def swap_state(model, state):
|
|
"""
|
|
Context manager that swaps the state of a model, e.g:
|
|
|
|
# model is in old state
|
|
with swap_state(model, new_state):
|
|
# model in new state
|
|
# model back to old state
|
|
"""
|
|
old_state = copy_state(model.state_dict())
|
|
model.load_state_dict(state, strict=False)
|
|
try:
|
|
yield
|
|
finally:
|
|
model.load_state_dict(old_state)
|
|
|
|
|
|
def capture_init(init):
|
|
@functools.wraps(init)
|
|
def __init__(self, *args, **kwargs):
|
|
self._init_args_kwargs = (args, kwargs)
|
|
init(self, *args, **kwargs)
|
|
|
|
return __init__
|