mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-12-01 02:27:21 +01:00
263 lines
8.9 KiB
Python
263 lines
8.9 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.
|
|
|
|
from dataclasses import dataclass
|
|
from concurrent import futures
|
|
from fnmatch import fnmatch
|
|
from functools import partial
|
|
import io
|
|
import math
|
|
from multiprocessing import cpu_count
|
|
import typing as tp
|
|
import zlib
|
|
|
|
import torch
|
|
|
|
|
|
class BaseQuantizer:
|
|
@dataclass
|
|
class _QuantizedParam:
|
|
name: str
|
|
param: torch.nn.Parameter
|
|
module: torch.nn.Module
|
|
# If a Parameter is used multiple times, `other` can be used
|
|
# to share state between the different Quantizers
|
|
other: tp.Optional[tp.Any]
|
|
|
|
def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
|
|
exclude: tp.Optional[tp.List[str]] = [], detect_bound: bool = True):
|
|
self.model = model
|
|
self.min_size = min_size
|
|
self.float16 = float16
|
|
self.exclude = exclude
|
|
self.detect_bound = detect_bound
|
|
self._quantized = False
|
|
self._pre_handle = self.model.register_forward_pre_hook(self._forward_pre_hook)
|
|
self._post_handle = self.model.register_forward_hook(self._forward_hook)
|
|
|
|
self._quantized_state = None
|
|
self._qparams = []
|
|
self._float16 = []
|
|
self._others = []
|
|
self._rnns = []
|
|
|
|
self._saved = []
|
|
|
|
self._find_params()
|
|
|
|
def _find_params(self):
|
|
min_params = self.min_size * 2**20 // 4
|
|
previous = {}
|
|
for module_name, module in self.model.named_modules():
|
|
if isinstance(module, torch.nn.RNNBase):
|
|
self._rnns.append(module)
|
|
for name, param in list(module.named_parameters(recurse=False)):
|
|
full_name = f"{module_name}.{name}"
|
|
matched = False
|
|
for pattern in self.exclude:
|
|
if fnmatch(full_name, pattern) or fnmatch(name, pattern):
|
|
matched = True
|
|
break
|
|
|
|
if param.numel() <= min_params or matched:
|
|
if id(param) in previous:
|
|
continue
|
|
if self.detect_bound:
|
|
previous[id(param)] = None
|
|
if self.float16:
|
|
self._float16.append(param)
|
|
else:
|
|
self._others.append(param)
|
|
else:
|
|
qparam = self._register_param(name, param, module, previous.get(id(param)))
|
|
if self.detect_bound:
|
|
previous[id(param)] = qparam
|
|
self._qparams.append(qparam)
|
|
|
|
def _register_param(self, name, param, module, other):
|
|
return self.__class__._QuantizedParam(name, param, module, other)
|
|
|
|
def _forward_pre_hook(self, module, input):
|
|
if self.model.training:
|
|
self._quantized_state = None
|
|
if self._quantized:
|
|
self.unquantize()
|
|
if self._pre_forward_train():
|
|
self._fix_rnns()
|
|
else:
|
|
self.quantize()
|
|
|
|
def _forward_hook(self, module, input, output):
|
|
if self.model.training:
|
|
if self._post_forward_train():
|
|
self._fix_rnns(flatten=False) # Hacky, next forward will flatten
|
|
|
|
def quantize(self, save=True):
|
|
"""
|
|
Immediately apply quantization to the model parameters.
|
|
If `save` is True, save a copy of the unquantized parameters, that can be
|
|
restored with `unquantize()`.
|
|
"""
|
|
if self._quantized:
|
|
return
|
|
if save:
|
|
self._saved = [qp.param.data.to('cpu', copy=True)
|
|
for qp in self._qparams if qp.other is None]
|
|
self.restore_quantized_state(self.get_quantized_state())
|
|
self._quantized = True
|
|
self._fix_rnns()
|
|
|
|
def unquantize(self):
|
|
"""
|
|
Revert a previous call to `quantize()`.
|
|
"""
|
|
if not self._quantized:
|
|
raise RuntimeError("Can only be called on a quantized model.")
|
|
if not self._saved:
|
|
raise RuntimeError("Nothing to restore.")
|
|
for qparam in self._qparams:
|
|
if qparam.other is None:
|
|
qparam.param.data[:] = self._saved.pop(0)
|
|
assert len(self._saved) == 0
|
|
self._quantized = False
|
|
self._fix_rnns()
|
|
|
|
def _pre_forward_train(self) -> bool:
|
|
"""
|
|
Called once before each forward for continuous quantization.
|
|
Should return True if parameters were changed.
|
|
"""
|
|
return False
|
|
|
|
def _post_forward_train(self) -> bool:
|
|
"""
|
|
Called once after each forward (to restore state for instance).
|
|
Should return True if parameters were changed.
|
|
"""
|
|
return False
|
|
|
|
def _fix_rnns(self, flatten=True):
|
|
"""
|
|
To be called after quantization happened to fix RNNs.
|
|
"""
|
|
for rnn in self._rnns:
|
|
rnn._flat_weights = [
|
|
(lambda wn: getattr(rnn, wn) if hasattr(rnn, wn) else None)(wn)
|
|
for wn in rnn._flat_weights_names]
|
|
if flatten:
|
|
rnn.flatten_parameters()
|
|
|
|
def get_quantized_state(self):
|
|
"""
|
|
Returns sufficient quantized information to rebuild the model state.
|
|
|
|
..Note::
|
|
To achieve maximum compression, you should compress this with
|
|
gzip or other, as quantized weights are not optimally coded!
|
|
"""
|
|
if self._quantized_state is None:
|
|
self._quantized_state = self._get_quantized_state()
|
|
return self._quantized_state
|
|
|
|
def _get_quantized_state(self):
|
|
"""
|
|
Actual implementation for `get_quantized_state`.
|
|
"""
|
|
float16_params = []
|
|
for p in self._float16:
|
|
q = p.data.half()
|
|
float16_params.append(q)
|
|
|
|
return {
|
|
"quantized": [self._quantize_param(qparam) for qparam in self._qparams
|
|
if qparam.other is None],
|
|
"float16": float16_params,
|
|
"others": [p.data.clone() for p in self._others],
|
|
}
|
|
|
|
def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
|
|
"""
|
|
To be overriden.
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
|
|
"""
|
|
To be overriden.
|
|
"""
|
|
raise NotImplementedError()
|
|
|
|
def restore_quantized_state(self, state) -> None:
|
|
"""
|
|
Restore the state of the model from the quantized state.
|
|
"""
|
|
for p, q in zip(self._float16, state["float16"]):
|
|
p.data[:] = q.to(p)
|
|
|
|
for p, q in zip(self._others, state["others"]):
|
|
p.data[:] = q
|
|
|
|
remaining = list(state["quantized"])
|
|
for qparam in self._qparams:
|
|
if qparam.other is not None:
|
|
# Only unquantize first appearance of nn.Parameter.
|
|
continue
|
|
quantized = remaining.pop(0)
|
|
qparam.param.data[:] = self._unquantize_param(qparam, quantized)
|
|
self._fix_rnns()
|
|
|
|
def detach(self) -> None:
|
|
"""
|
|
Detach from the model, removes hooks and anything else.
|
|
"""
|
|
self._pre_handle.remove()
|
|
self._post_handle.remove()
|
|
|
|
def model_size(self) -> torch.Tensor:
|
|
"""
|
|
Returns an estimate of the quantized model size.
|
|
"""
|
|
total = torch.tensor(0.)
|
|
for p in self._float16:
|
|
total += 16 * p.numel()
|
|
for p in self._others:
|
|
total += 32 * p.numel()
|
|
return total / 2**20 / 8 # bits to MegaBytes
|
|
|
|
def true_model_size(self) -> float:
|
|
"""
|
|
Return the true quantized model size, in MB, without extra
|
|
compression.
|
|
"""
|
|
return self.model_size().item()
|
|
|
|
def compressed_model_size(self, compress_level=-1, num_workers=8) -> float:
|
|
"""
|
|
Return the compressed quantized model size, in MB.
|
|
|
|
Args:
|
|
compress_level (int): compression level used with zlib,
|
|
see `zlib.compress` for details.
|
|
num_workers (int): will split the final big byte representation in that
|
|
many chunks processed in parallels.
|
|
"""
|
|
out = io.BytesIO()
|
|
torch.save(self.get_quantized_state(), out)
|
|
ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers)
|
|
return ms / 2 ** 20
|
|
|
|
|
|
def _compress_len(data, compress_level):
|
|
return len(zlib.compress(data, level=compress_level))
|
|
|
|
|
|
def _parallel_compress_len(data, compress_level, num_workers):
|
|
num_workers = min(cpu_count(), num_workers)
|
|
chunk_size = int(math.ceil(len(data) / num_workers))
|
|
chunks = [data[offset:offset + chunk_size] for offset in range(0, len(data), chunk_size)]
|
|
with futures.ProcessPoolExecutor(num_workers) as pool:
|
|
return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))
|