Cool_Tools/ultimatevocalremovergui
1
0
Fork 0
mirror of https://github.com/Anjok07/ultimatevocalremovergui.git synced 2025-01-31 04:13:45 +01:00
Code Issues Packages Projects Releases Wiki Activity

Delete diffq directory

Browse Source
This commit is contained in:
Anjok07 2022-07-26 04:56:39 -05:00 committed by GitHub
parent 8def1cb6c5
commit cba7e1c4f6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 0 additions and 1364 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
diffq/__init__.py
View File

@ -1,29 +0,0 @@
# 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.
# flake8: noqa
"""
This package implements different quantization strategies:
- `diffq.uniform.UniformQuantizer`: classic uniform quantization over n bits.
- `diffq.diffq.DiffQuantizer`: differentiable quantizer based on scaled noise injection.
- `diffq.lsq.LSQ`: Learnt Step size Quantizer based on [Esser et al. 2019] https://arxiv.org/abs/1902.08153
- `diffq.bitpack`: efficient CPU bit-packing for returning quantized states.
- `diffq.torch_pack`: torch based bit-packing compatible with torchscript.
- `diffq.ts_export`: support exporting DiffQ based models to torchscript.
Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
"""
from .uniform import UniformQuantizer
from .diffq import DiffQuantizer
from .lsq import LSQ
from .base import restore_quantized_state
from . import ts_export
__version__ = "0.2.2"

343
diffq/base.py
View File

@ -1,343 +0,0 @@
# 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.
"""Base class for all quantizers."""
from contextlib import contextmanager
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 pickle
import typing as tp
import zlib
import torch
from . import bitpack
from . import torch_pack as torch_pack_mod
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._need_unquantize = None
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._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
self.unquantize()
if self._pre_forward_train():
self._fix_rnns()
else:
assert self._need_unquantize is None
self._need_unquantize = 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
else:
if self._need_unquantize:
self._need_unquantize = None
self.unquantize()
def quantize(self):
"""
Immediately apply quantization to the model parameters.
Model parameters are saved to later allow restoring the unquantized state.
Note that you shouldn't need to call this for model evaluation, as long as
you properly call `model.train()` and `model.eval()`, but this can be
useful for weight inspection.
"""
if self._quantized:
return False
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(packed=False))
self._quantized = True
self._fix_rnns()
return True
@contextmanager
def enter_quantize(self):
"""Context manager for entering quantized state."""
self.quantize()
try:
yield
finally:
self.unquantize()
def unquantize(self):
"""
Revert a previous call to `quantize()`.
"""
if not self._quantized:
return
if not self._saved:
raise RuntimeError("Nothing to restore. This shouldn't happen")
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 _bit_pack_param(self, qparam: _QuantizedParam, quantized: tp.Any,
pack_fn: tp.Any) -> tp.Any:
"""Further bitpack the quantized representation.
This is used to return the quantized state. Should be overriden.
"""
return quantized
def _bit_unpack_param(self, qparam: _QuantizedParam, packed: tp.Any,
unpack_fn: tp.Any) -> tp.Any:
"""Unpack bitpacked representation. Should be overriden
"""
return packed
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 get_quantized_state(self, packed=True, torch_pack=False):
"""
Return a quantized representation fo the weights. If `packed` is True,
this will also perform bitpacking to ensure optimal store.
If `torck_pack` is true, the bitpacking from `torch_pack` will be used.
It is slower (except maybe on GPU), but is compatible with torchscript.
You can restore a model from a quantized state either using
`BaseQuantizer.restore_quantized_state` or `diffq.restore_quantized_state`
if you do not have the original quantizer around anymore.
"""
float16_params = []
for p in self._float16:
q = p.data.half()
float16_params.append(q)
if torch_pack:
pack_fn = torch_pack_mod.pack
else:
pack_fn = bitpack.pack
all_quantized = []
for qparam in self._qparams:
if qparam.other is not None:
continue
quantized = self._quantize_param(qparam)
if packed:
quantized = self._bit_pack_param(qparam, quantized, pack_fn=pack_fn)
all_quantized.append(quantized)
state = {
"quantized": all_quantized,
"float16": float16_params,
"others": [p.data.clone() for p in self._others],
}
kwargs = dict(self._init_kwargs)
kwargs.pop("model")
state["meta"] = {
"init_kwargs": kwargs,
"klass": self.__class__,
"packed": packed,
"torch_pack": torch_pack
}
return state
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
meta = state.get("meta", {})
packed = meta.get("packed", False)
torch_pack = meta.get("torch_pack", False)
if torch_pack:
unpack_fn = torch_pack_mod.unpack
else:
unpack_fn = bitpack.unpack
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)
if packed:
quantized = self._bit_unpack_param(qparam, quantized, unpack_fn)
qparam.param.data[:] = self._unquantize_param(qparam, quantized)
assert not remaining
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 packed_model_size(self) -> float:
"""Return the packed model size, when stored with pickle.
This should be mostly equivalent to `true_model_size` up to some
slight overhead for storing metadata.
"""
state = self.get_quantized_state(packed=True)
return len(pickle.dumps(state)) / 2 ** 20
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(packed=False), out)
ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers)
return ms / 2 ** 20
def restore_quantized_state(model: torch.nn.Module, state: dict):
assert "meta" in state
quantizer = state["meta"]["klass"](model, **state["meta"]["init_kwargs"])
quantizer.restore_quantized_state(state)
quantizer.detach()
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.ThreadPoolExecutor(num_workers) as pool:
# thread pool is okay here, zlib calls an external C lib and GIL is released
# before the call.
return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))

BIN
diffq/bitpack.cp39-win_amd64.pyd
View File

Binary file not shown.

318
diffq/diffq.py
View File

@ -1,318 +0,0 @@
# 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.
"""
Differentiable quantizer based on scaled noise injection.
"""
from dataclasses import dataclass
import math
import typing as tp
import torch
from .base import BaseQuantizer
from .uniform import uniform_quantize, uniform_unquantize
from .utils import capture_init, simple_repr
class DiffQuantizer(BaseQuantizer):
@dataclass
class _QuantizedParam(BaseQuantizer._QuantizedParam):
logit: torch.nn.Parameter
@capture_init
def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
group_size: int = 1, min_bits: float = 2, max_bits: float = 15,
param="bits", noise="gaussian",
init_bits: float = 8, extra_bits: float = 0, suffix: str = "_diffq",
exclude: tp.List[str] = [], detect_bound: bool = True):
"""
Differentiable quantizer based on scaled noise injection.
For every parameter `p` in the model, this introduces a number of bits parameter
`b` with the same dimensions (when group_size = 1).
Before each forward, `p` is replaced by `p + U`
with U uniform iid noise with range [-d/2, d/2], with `d` the uniform quantization
step for `b` bits.
This noise approximates the quantization noise in a differentiable manner, both
with respect to the unquantized parameter `p` and the number of bits `b`.
At eveluation (as detected with `model.eval()`), the model is replaced
by its true quantized version, and restored when going back to training.
When doing actual quantization (for serialization, or evaluation),
the number of bits is rounded to the nearest integer, and needs to be stored along.
This will cost a few bits per dimension. To reduce this cost, one can use `group_size`,
which will use a single noise level for multiple weight entries.
You can use the `DiffQuantizer.model_size` method to get a differentiable estimate of the
model size in MB. You can then use this estimate as a penalty in your training loss.
Args:
model (torch.nn.Module): model to quantize
min_size (float): minimum size in MB of a parameter to be quantized.
float16 (bool): if a layer is smaller than min_size, should we still do float16?
group_size (int): weight entries are groupped together to reduce the number
of noise scales to store. This should divide the size of all parameters
bigger than min_size.
min_bits (float): minimal number of bits.
max_bits (float): maximal number of bits.
init_bits (float): initial number of bits.
extra_bits (float): extra bits to add for actual quantization (before roundoff).
suffix (str): suffix used for the name of the extra noise scale parameters.
exclude (list[str]): list of patterns used to match parameters to exclude.
For instance `['bias']` to exclude all bias terms.
detect_bound (bool): if True, will detect bound parameters and reuse
the same quantized tensor for both, as well as the same number of bits.
..Warning::
You must call `model.training()` and `model.eval()` for `DiffQuantizer` work properly.
"""
self.group_size = group_size
self.min_bits = min_bits
self.max_bits = max_bits
self.init_bits = init_bits
self.extra_bits = extra_bits
self.suffix = suffix
self.param = param
self.noise = noise
assert noise in ["gaussian", "uniform"]
self._optimizer_setup = False
self._min_noise = 1 / (2 ** self.max_bits - 1)
self._max_noise = 1 / (2 ** self.min_bits - 1)
assert group_size >= 0
assert min_bits < init_bits < max_bits, \
"init_bits must be between min_bits and max_bits excluded3"
for name, _ in model.named_parameters():
if name.endswith(suffix):
raise RuntimeError("The model already has some noise scales parameters, "
"maybe you used twice a DiffQuantizer on the same model?.")
super().__init__(model, min_size, float16, exclude, detect_bound)
def _get_bits(self, logit: torch.Tensor):
if self.param == "noise":
return torch.log2(1 + 1 / self._get_noise_scale(logit))
else:
t = torch.sigmoid(logit)
return self.max_bits * t + (1 - t) * self.min_bits
def _get_noise_scale(self, logit: torch.Tensor):
if self.param == "noise":
t = torch.sigmoid(logit)
return torch.exp(t * math.log(self._min_noise) + (1 - t) * math.log(self._max_noise))
else:
return 1 / (2 ** self._get_bits(logit) - 1)
def _register_param(self, name, param, module, other):
if other is not None:
return self.__class__._QuantizedParam(
name=name, param=param, module=module, logit=other.logit, other=other)
assert self.group_size == 0 or param.numel() % self.group_size == 0
# we want the initial number of bits to be init_bits.
if self.param == "noise":
noise_scale = 1 / (2 ** self.init_bits - 1)
t = (math.log(noise_scale) - math.log(self._max_noise)) / (
math.log(self._min_noise) - math.log(self._max_noise))
else:
t = (self.init_bits - self.min_bits) / (self.max_bits - self.min_bits)
assert 0 < t < 1
logit = torch.logit(torch.tensor(float(t)))
assert abs(self._get_bits(logit) - self.init_bits) < 1e-5
if self.group_size > 0:
nparam = param.numel() // self.group_size
else:
nparam = 1
logit = torch.nn.Parameter(
torch.full(
(nparam,),
logit,
device=param.device))
module.register_parameter(name + self.suffix, logit)
return self.__class__._QuantizedParam(
name=name, param=param, module=module, logit=logit, other=None)
def clear_optimizer(self, optimizer: torch.optim.Optimizer):
params = [qp.logit for qp in self._qparams]
for group in optimizer.param_groups:
new_params = []
for q in list(group["params"]):
matched = False
for p in params:
if p is q:
matched = True
if not matched:
new_params.append(q)
group["params"][:] = new_params
def setup_optimizer(self, optimizer: torch.optim.Optimizer,
lr: float = 1e-3, **kwargs):
"""
Setup the optimizer to tune the number of bits. In particular, this will deactivate
weight decay for the bits parameters.
Args:
optimizer (torch.Optimizer): optimizer to use.
lr (float): specific learning rate for the bits parameters. 1e-3
is perfect for Adam.,w
kwargs (dict): overrides for other optimization parameters for the bits.
"""
assert not self._optimizer_setup
self._optimizer_setup = True
params = [qp.logit for qp in self._qparams]
for group in optimizer.param_groups:
for q in list(group["params"]):
for p in params:
if p is q:
raise RuntimeError("You should create the optimizer "
"before the quantizer!")
group = {"params": params, "lr": lr, "weight_decay": 0}
group.update(kwargs)
optimizer.add_param_group(group)
def no_optimizer(self):
"""
Call this if you do not want to use an optimizer.
"""
self._optimizer_setup = True
def check_unused(self):
for qparam in self._qparams:
if qparam.other is not None:
continue
grad = qparam.param.grad
if grad is None or (grad == 0).all():
if qparam.logit.grad is not None:
qparam.logit.grad.data.zero_()
def model_size(self, exact=False):
"""
Differentiable estimate of the model size.
The size is returned in MB.
If `exact` is True, then the output is no longer differentiable but
reflect exactly an achievable size, even without compression,
i.e.same as returned by `naive_model_size()`.
"""
total = super().model_size()
subtotal = 0
for qparam in self._qparams:
# only count the first appearance of a Parameter
if qparam.other is not None:
continue
bits = self.extra_bits + self._get_bits(qparam.logit)
if exact:
bits = bits.round().clamp(1, 15)
if self.group_size == 0:
group_size = qparam.param.numel()
else:
group_size = self.group_size
subtotal += group_size * bits.sum()
subtotal += 2 * 32 # param scale
# Number of bits to represent each number of bits
bits_bits = math.ceil(math.log2(1 + (bits.max().round().item() - self.min_bits)))
subtotal += 8 # 8 bits for bits_bits
subtotal += bits_bits * bits.numel()
subtotal /= 2 ** 20 * 8 # bits -> MegaBytes
return total + subtotal
def true_model_size(self):
"""
Naive model size without zlib compression.
"""
return self.model_size(exact=True).item()
def _pre_forward_train(self):
if not self._optimizer_setup:
raise RuntimeError("You must call `setup_optimizer()` on your optimizer "
"before starting training.")
for qparam in self._qparams:
if qparam.other is not None:
noisy = qparam.other.module._parameters[qparam.other.name]
else:
bits = self._get_bits(qparam.logit)[:, None]
if self.group_size == 0:
p_flat = qparam.param.view(-1)
else:
p_flat = qparam.param.view(-1, self.group_size)
scale = p_flat.max() - p_flat.min()
unit = 1 / (2**bits - 1)
if self.noise == "uniform":
noise_source = (torch.rand_like(p_flat) - 0.5)
elif self.noise == "gaussian":
noise_source = torch.randn_like(p_flat) / 2
noise = scale * unit * noise_source
noisy = p_flat + noise
# We bypass the checks by PyTorch on parameters being leafs
qparam.module._parameters[qparam.name] = noisy.view_as(qparam.param)
return True
def _post_forward_train(self):
for qparam in self._qparams:
qparam.module._parameters[qparam.name] = qparam.param
return True
def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
bits = self.extra_bits + self._get_bits(qparam.logit)
bits = bits.round().clamp(1, 15)[:, None].byte()
if self.group_size == 0:
p = qparam.param.data.view(1, -1)
else:
p = qparam.param.data.view(-1, self.group_size)
levels, scales = uniform_quantize(p, bits)
return levels, scales, bits[:, 0]
def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
levels, param_scale, bits = quantized
bits = bits[:, None]
return uniform_unquantize(levels, param_scale, bits).view_as(qparam.param.data)
def _bit_pack_param(self, qparam, quantized, pack_fn):
levels, scales, bits = quantized
all_packed = []
for bit in range(1, 15):
sub_levels = levels[bits == bit]
if not sub_levels.numel():
all_packed.append(None)
else:
packed = pack_fn(sub_levels, bit)
all_packed.append(packed)
packed_bits = pack_fn(bits - self.min_bits)
return (all_packed, scales, packed_bits)
def _bit_unpack_param(self, qparam, packed, unpack_fn):
"""Unpack bitpacked representation. Should be overriden.
"""
packed_all_levels, scales, packed_bits = packed
bits = unpack_fn(packed_bits, qparam.logit.numel()) + self.min_bits
bits = bits.to(qparam.param.device)
levels = torch.empty(qparam.logit.numel(), self.group_size,
dtype=torch.short, device=qparam.param.device)
for idx, packed_levels in enumerate(packed_all_levels):
bit = idx + 1
if packed_levels is None:
continue
sub_levels = levels[bits == bit]
levels[bits == bit] = unpack_fn(
packed_levels, sub_levels.numel()).view_as(sub_levels).to(sub_levels)
return (levels, scales, bits)
def detach(self):
super().detach()
for qparam in self._qparams:
delattr(qparam.module, qparam.name + self.suffix)
def __repr__(self):
return simple_repr(self)

192
diffq/lsq.py
View File

@ -1,192 +0,0 @@
# 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.
"""
Learnt-Stepsize quantizer from [Esser et al. 2019] https://arxiv.org/abs/1902.08153.
"""
from dataclasses import dataclass
import typing as tp
import torch
from .base import BaseQuantizer
from .utils import capture_init, simple_repr
class LSQ(BaseQuantizer):
"""Implements weight only quantization based on [Esser et al. 2019].
https://arxiv.org/abs/1902.08153
"""
@dataclass
class _QuantizedParam(BaseQuantizer._QuantizedParam):
scale: torch.nn.Parameter
@capture_init
def __init__(self, model: torch.nn.Module, bits: int = 8, min_size: float = 0.01,
float16: bool = False, suffix: str = "_lsq", exclude=[], detect_bound=True):
assert 0 < bits <= 15
self.suffix = suffix
self._optimizer_setup = False
self.bits = bits
for name, _ in model.named_parameters():
if name.endswith(suffix):
raise RuntimeError("The model already has some noise scales parameters, "
"maybe you used twice a LSQ on the same model?.")
super().__init__(model, min_size, float16, exclude, detect_bound)
def _register_param(self, name, param, module, other):
if other is not None:
return self.__class__._QuantizedParam(
name=name, param=param, module=module, scale=other.scale, other=other)
# we want the initial number of bits to be init_bits.
scale = 2 * param.data.abs().mean() / (2 ** (self.bits - 1))**0.5
scale = torch.nn.Parameter(scale)
module.register_parameter(name + self.suffix, scale)
return self.__class__._QuantizedParam(
name=name, param=param, module=module, scale=scale, other=None)
def clear_optimizer(self, optimizer: torch.optim.Optimizer):
params = [qp.scale for qp in self._qparams]
for group in optimizer.param_groups:
new_params = []
for q in list(group["params"]):
matched = False
for p in params:
if p is q:
matched = True
if not matched:
new_params.append(q)
group["params"][:] = new_params
def setup_optimizer(self, optimizer: torch.optim.Optimizer, **kwargs):
"""
Setup the optimizer to tune the scale parameter.
Following [Esser et al. 2019], we use the same LR and weight decay
as the base optimizer, unless specified otherwise.
Args:
optimizer (torch.Optimizer): optimizer to use.
kwargs (dict): overrides for optimization parameters
"""
assert not self._optimizer_setup
self._optimizer_setup = True
params = [qp.scale for qp in self._qparams]
for group in optimizer.param_groups:
for q in list(group["params"]):
for p in params:
if p is q:
raise RuntimeError("You should create the optimizer "
"before the quantizer!")
group = {"params": params}
group.update(kwargs)
optimizer.add_param_group(group)
def no_optimizer(self):
"""
Call this if you do not want to use an optimizer.
"""
self._optimizer_setup = True
def model_size(self, exact=False):
"""
Differentiable estimate of the model size.
The size is returned in MB.
If `exact` is True, then the output is no longer differentiable but
reflect exactly an achievable size, even without compression,
i.e.same as returned by `naive_model_size()`.
"""
total = super().model_size()
subtotal = 0
for qparam in self._qparams:
# only count the first appearance of a Parameter
if qparam.other is not None:
continue
bits = qparam.param.numel() * self.bits
subtotal += bits
subtotal += 1 * 32 # param scale
subtotal /= 2 ** 20 * 8 # bits -> MegaBytes
return total + subtotal
def true_model_size(self):
"""
Naive model size without zlib compression.
"""
return self.model_size(exact=True).item()
def _pre_forward_train(self):
if not self._optimizer_setup:
raise RuntimeError("You must call `setup_optimizer()` on your optimizer "
"before starting training.")
for qparam in self._qparams:
scale = qparam.scale
quant, _ = quantize(qparam.param, scale, self.bits)
# We bypass the checks by PyTorch on parameters being leafs
qparam.module._parameters[qparam.name] = quant
return True
def _post_forward_train(self):
for qparam in self._qparams:
qparam.module._parameters[qparam.name] = qparam.param
return True
def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
_, index = quantize(qparam.param, qparam.scale, self.bits)
assert (index <= (2 ** (self.bits - 1) - 1)).all(), index.max()
assert (index >= (-2 ** (self.bits - 1))).all(), index.min()
return index.detach().short(), qparam.scale.detach()
def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
index, scale = quantized
return index.float() * scale
def _bit_pack_param(self, qparam, quantized, pack_fn):
levels, scale = quantized
packed = pack_fn(levels + 2 ** (self.bits - 1))
return (packed, scale)
def _bit_unpack_param(self, qparam, packed, unpack_fn):
"""Unpack bitpacked representation. Should be overriden
"""
packed_levels, scale = packed
levels = unpack_fn(
packed_levels, qparam.param.numel()).to(qparam.param.device).view_as(qparam.param)
levels -= 2 ** (self.bits - 1)
return (levels, scale)
def detach(self):
super().detach()
for qparam in self._qparams:
delattr(qparam.module, qparam.name + self.suffix)
def __repr__(self):
return simple_repr(self)
def roundpass(x):
return (x.round() - x).detach() + x
def gradscale(x, scale):
return (x - x * scale).detach() + x * scale
def quantize(tensor, scale, bits):
low = - 2 ** (bits - 1)
high = 2 ** (bits - 1) - 1
scale = gradscale(scale, 1 / (tensor.numel() * high)**0.5)
index = tensor / scale
index = index.clamp(low, high)
index = roundpass(index)
return index * scale, index

80
diffq/torch_pack.py
View File

@ -1,80 +0,0 @@
# 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.
"""Bit packing in pure PyTorch.
Slower than bitpack.pyx but compatible with torchscript.
"""
import math
import typing as tp
import torch
from torch.nn import functional as F
def as_rectangle(p: torch.Tensor, side: int):
"""Reshape as rectangle, using padding when necessary so that out shape is [*, side]"""
p_flat = p.view(-1)
ideal_length = int(math.ceil(len(p_flat) / side) * side)
p_flat_pad = F.pad(p_flat, (0, ideal_length - len(p_flat)))
return p_flat_pad.view(side, -1)
def _storage_size(dtype: torch.dtype):
if dtype == torch.int64:
return 64
elif dtype == torch.int32:
return 32
elif dtype == torch.int16:
return 16
elif dtype == torch.uint8:
return 8
else:
raise ValueError("Invalid bitpacking storage type")
def pack(indexes, nbits: int = 0, storage_dtype: torch.dtype = torch.int16):
"""You can think of indexes as a "Tensor" of bits of shape [L, nbits].
Instead of concatenating naively as [L * nbits], we instead look at it transposed as
[nbits, L]. For L = 16 * G, we get [nbits, G, 16] which is trivial to store
efficiently on int16 integers.
There will be overhead if L is far from a multiple of 16 (e.g. 1) but for large
model layers this is acceptable. Storage type can be changed.
`nbits` should be the number of bits on which the indexes are coded, and will
actually be determined automatically if set to 0.
"""
assert not indexes.dtype.is_floating_point
if indexes.numel() > 0:
assert indexes.max().item() < 2 ** 15
assert indexes.min().item() >= 0
if nbits == 0:
nbits = int(math.ceil(math.log2(1 + (indexes.max()))))
else:
assert indexes.max().item() < 2 ** nbits
indexes = indexes.reshape(-1)
storage_size = _storage_size(storage_dtype)
rect = as_rectangle(indexes, storage_size)
out = torch.zeros(nbits, rect.shape[1], dtype=storage_dtype, device=indexes.device)
for in_bit in range(nbits):
for out_bit in range(storage_size):
d = ((rect[out_bit] >> in_bit) & 1).to(out.dtype) << out_bit
out[in_bit, :] |= d
return out
def unpack(packed: torch.Tensor, length: tp.Optional[int] = None):
"""Opposite of `pack`. You might need to specify the original length."""
storage_size = _storage_size(packed.dtype)
nbits, groups = packed.shape
out = torch.zeros(storage_size, groups, dtype=torch.int16, device=packed.device)
for in_bit in range(storage_size):
for out_bit in range(nbits):
bit_value = (packed[out_bit, :] >> in_bit) & 1
out[in_bit, :] = out[in_bit, :] | (bit_value.to(out) << out_bit)
out = out.view(-1)
if length is not None:
out = out[:length]
return out

209
diffq/ts_export.py
View File

@ -1,209 +0,0 @@
# 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.
"""TorchScript export support.
We have to do a lot of black magic for TorchScript to be happy
because we cannot dynamically allocate new weights when loading the model.
Here is how it works:
- we generate code in a temporary python file for the given model that explicitely
override all the weights on the first forward from their packed version.
This is because TorchScript does not let us iterate over parameters in a generic manner.
- we zero out all the original weights. We cannot simply remove those weights
because TorchScript won't let us recreate them.
- A TorchScript file is just a zip file, but stored without compression.
In order to remove the cost of storing the zeroed out weights, we unzip the file,
and zip it again with compression.
"""
import importlib
import os
from pathlib import Path
import random
import sys
import typing as tp
import tempfile
import zipfile
import torch
from torch import jit
from .diffq import DiffQuantizer
from .uniform import uniform_unquantize
from .torch_pack import unpack
_DiffQPacked = tp.Tuple[
tp.List[tp.Optional[torch.Tensor]], tp.Tuple[float, float],
torch.Tensor, tp.List[int]]
# This is the template for the generated class.
TEMPLATE = '''
import typing as tp
import torch
from torch import jit
from diffq.ts_export import _unpack_param, _DiffQPacked
from {module} import {klass}
class DiffQTSModel(torch.nn.Module):
def __init__(self, model: {klass}, group_size: int, min_bits: int,
packed: tp.List[_DiffQPacked]):
super().__init__()
self.group_size = group_size
self.min_bits = min_bits
self.model = model
self._unpacked = False
self._packed = packed
@jit.export
def unpack(self):
"""
Unpack the weights, automatically called on the first forward,
or explicitely."""
if self._unpacked:
return
{unpack_assigns}
self._unpacked = True
def forward(self, x: torch.Tensor):
self.unpack()
return self.model.forward(x)
'''
# those are the assignments for each quantized weight.
UNPACK_ASSIGN = (' ' * 8) + ('self.model{full_name}.data[:] = '
'_unpack_param(self._packed[{index}], '
'group_size=self.group_size, min_bits=self.min_bits)')
UNPACK_ASSIGN_SAME = (' ' * 8) + 'self.model{full_name} = self.model{other_name}'
def export(quantizer: DiffQuantizer, path: tp.Union[str, Path]):
"""Export the given quantized model to the given path.
We must save the quantized model ourselves, as we need to recompress
the zip archive afterwards.
"""
packed: tp.List[_DiffQPacked] = []
uniq_name = ''.join([random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(12)])
with tempfile.TemporaryDirectory() as tmpdir:
sys.path.insert(0, tmpdir)
try:
code = _codegen(quantizer)
with open(Path(tmpdir) / f'{uniq_name}.py', 'w') as f:
f.write(code)
module = importlib.import_module(uniq_name)
ts_klass = module.DiffQTSModel
state = quantizer.get_quantized_state(packed=True, torch_pack=True)
quantized = state["quantized"]
for qparam in quantizer._qparams:
if qparam.other is None:
levels, scales, bits = quantized.pop(0)
size = qparam.param.size()
packed.append((levels, scales, bits, list(size)))
qparam.param.data.zero_()
quantizer.detach()
ts_premodel = ts_klass(quantizer.model, quantizer.group_size,
quantizer.min_bits, packed)
ts_model = jit.script(ts_premodel)
if path is not None:
jit.save(ts_model, path)
recompress(path)
finally:
sys.path.pop(0)
return ts_model
def _unpack_param(packed: _DiffQPacked, group_size: int, min_bits: int) -> torch.Tensor:
"""Function called from TorchScript on the first forward to decode the
packed weights to FP32.
"""
packed_all_levels, scales, packed_bits, shape = packed
numel = 1
for dim in shape:
numel *= dim
bits = unpack(packed_bits, numel // group_size) + min_bits
levels = torch.empty(bits.numel(), group_size, dtype=torch.short)
for idx, packed_levels in enumerate(packed_all_levels):
bit = idx + 1
if packed_levels is not None:
sub_levels = levels[bits == bit]
levels[bits == bit] = unpack(packed_levels, sub_levels.numel()).view_as(sub_levels)
bits = bits[:, None]
unquant = uniform_unquantize(levels, scales, bits)
if len(shape) == 4:
return unquant.view(shape[0], shape[1], shape[2], shape[3])
elif len(shape) == 3:
return unquant.view(shape[0], shape[1], shape[2])
elif len(shape) == 2:
return unquant.view(shape[0], shape[1])
elif len(shape) == 1:
return unquant.view(shape[0])
else:
raise RuntimeError("Invalid numbr of dim")
def recompress(path: tp.Union[str, Path]):
"""After having saved the torchscript file, this will recompress it
to make sure all the zeroed out parameters don't actually take any space.
"""
with tempfile.TemporaryDirectory() as tmpdir:
with zipfile.ZipFile(path) as zipin:
zipin.extractall(tmpdir)
with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_DEFLATED,
compresslevel=1) as zipout:
for root, folders, files in os.walk(tmpdir):
for file in files:
fp = Path(root) / file
name = fp.relative_to(tmpdir)
zipout.write(fp, name)
def _get_full_name_access(full_name):
# When generating code, we need to handle attributes vs. indexing.
parts = []
for part in full_name.split("."):
try:
index = int(part)
except ValueError:
parts.append("." + part)
else:
parts.append(f"[{index}]")
return "".join(parts)
def _codegen(quantizer: DiffQuantizer):
# Generates the code for the given quantizer
module = quantizer.model.__class__.__module__
klass = quantizer.model.__class__.__name__
model = quantizer.model
assert not quantizer.float16
names = {}
for mod_name, mod in model.named_modules():
names[mod] = mod_name
unpack_assigns = []
index = 0
for qparam in quantizer._qparams:
mod_name = names[qparam.module]
if mod_name == '':
full_name = qparam.name
else:
full_name = mod_name + '.' + qparam.name
full_name = _get_full_name_access(full_name)
if qparam.other is None:
unpack_assigns.append(UNPACK_ASSIGN.format(full_name=full_name, index=index))
index += 1
else:
other_name = names[(qparam.other.module, qparam.other.name)]
other_name = _get_full_name_access(other_name)
unpack_assigns.append(
UNPACK_ASSIGN_SAME.format(full_name=full_name, other_name=other_name))
return TEMPLATE.format(
module=module,
klass=klass,
unpack_assigns='\n'.join(unpack_assigns))

135
diffq/uniform.py
View File

@ -1,135 +0,0 @@
# 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.
"""
Classic uniform quantization over n bits.
"""
from typing import Tuple
import torch
from .base import BaseQuantizer
from .utils import capture_init, simple_repr
def uniform_quantize(p: torch.Tensor, bits: torch.Tensor = torch.tensor(8.)):
"""
Quantize the given weights over `bits` bits.
Returns:
- quantized levels
- (min, max) range.
"""
assert (bits >= 1).all() and (bits <= 15).all()
num_levels = (2 ** bits.float()).long()
mn = p.min().item()
mx = p.max().item()
p = (p - mn) / (mx - mn) # put p in [0, 1]
unit = 1 / (num_levels - 1) # quantization unit
levels = (p / unit).round()
if (bits <= 8).all():
levels = levels.byte()
else:
levels = levels.short()
return levels, (mn, mx)
def uniform_unquantize(levels: torch.Tensor, scales: Tuple[float, float],
bits: torch.Tensor = torch.tensor(8.)):
"""
Unquantize the weights from the levels and scale. Return a float32 tensor.
"""
mn, mx = scales
num_levels = 2 ** bits.float()
unit = 1 / (num_levels - 1)
levels = levels.float()
p = levels * unit # in [0, 1]
return p * (mx - mn) + mn
class UniformQuantizer(BaseQuantizer):
@capture_init
def __init__(self, model: torch.nn.Module, bits: float = 8., min_size: float = 0.01,
float16: bool = False, qat: bool = False, exclude=[], detect_bound=True):
"""
Args:
model (torch.nn.Module): model to quantize
bits (float): number of bits to quantize over.
min_size (float): minimum size in MB of a parameter to be quantized.
float16 (bool): if a layer is smaller than min_size, should we still do float16?
qat (bool): perform quantized aware training.
exclude (list[str]): list of patterns used to match parameters to exclude.
For instance `['bias']` to exclude all bias terms.
detect_bound (bool): if True, will detect bound parameters and reuse
the same quantized tensor for both.
"""
self.bits = float(bits)
self.qat = qat
super().__init__(model, min_size, float16, exclude, detect_bound)
def __repr__(self):
return simple_repr(self, )
def _pre_forward_train(self):
if self.qat:
for qparam in self._qparams:
if qparam.other is not None:
new_param = qparam.other.module._parameters[qparam.other.name]
else:
quantized = self._quantize_param(qparam)
qvalue = self._unquantize_param(qparam, quantized)
new_param = qparam.param + (qvalue - qparam.param).detach()
qparam.module._parameters[qparam.name] = new_param
return True
return False
def _post_forward_train(self):
if self.qat:
for qparam in self._qparams:
qparam.module._parameters[qparam.name] = qparam.param
return True
return False
def _quantize_param(self, qparam):
levels, scales = uniform_quantize(qparam.param.data, torch.tensor(self.bits))
return (levels, scales)
def _unquantize_param(self, qparam, quantized):
levels, scales = quantized
return uniform_unquantize(levels, scales, torch.tensor(self.bits))
def _bit_pack_param(self, qparam, quantized, pack_fn):
levels, scales = quantized
packed = pack_fn(levels, self.bits)
return (packed, scales)
def _bit_unpack_param(self, qparam, packed, unpack_fn):
"""Unpack bitpacked representation. Should be overriden
"""
packed_levels, scales = packed
levels = unpack_fn(
packed_levels, qparam.param.numel()).to(qparam.param.device).view_as(qparam.param)
return (levels, scales)
def model_size(self):
"""
Non differentiable model size in MB.
"""
total = super().model_size()
subtotal = 0
for qparam in self._qparams:
if qparam.other is None: # if parameter is bound, count only one copy.
subtotal += self.bits * qparam.param.numel() + 64 # 2 float for the overall scales
subtotal /= 2**20 * 8 # bits to MegaBytes
return total + subtotal
def true_model_size(self):
"""
Return the true quantized model size, in MB, without extra
compression.
"""
return self.model_size().item()

58
diffq/utils.py
View File

@ -1,58 +0,0 @@
# 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.
import functools
import inspect
from typing import Optional, List
def simple_repr(obj, attrs: Optional[List[str]] = None, overrides={}):
"""
Return a simple representation string for `obj`.
If `attrs` is not None, it should be a list of attributes to include.
"""
params = inspect.signature(obj.__class__).parameters
attrs_repr = []
if attrs is None:
attrs = params.keys()
for attr in attrs:
display = False
if attr in overrides:
value = overrides[attr]
elif hasattr(obj, attr):
value = getattr(obj, attr)
else:
continue
if attr in params:
param = params[attr]
if param.default is inspect._empty or value != param.default:
display = True
else:
display = True
if display:
attrs_repr.append(f"{attr}={value}")
return f"{obj.__class__.__name__}({','.join(attrs_repr)})"
def capture_init(init):
"""capture_init.
Decorate `__init__` with this, and you can then
recover the *args and **kwargs passed to it in `self._init_args_kwargs`
"""
signature = inspect.signature(init)
@functools.wraps(init)
def __init__(self, *args, **kwargs):
bound = signature.bind(self, *args, **kwargs)
actual_kwargs = dict(bound.arguments)
del actual_kwargs['self']
actual_kwargs.update(bound.kwargs)
self._init_kwargs = actual_kwargs
init(self, *args, **kwargs)
return __init__