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diffq/__init__.py
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# 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.
Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
"""
from .uniform import UniformQuantizer
from .diffq import DiffQuantizer

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diffq/base.py
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# 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))

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diffq/diffq.py
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# 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 simple_repr
class DiffQuantizer(BaseQuantizer):
@dataclass
class _QuantizedParam(BaseQuantizer._QuantizedParam):
logit: torch.nn.Parameter
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)
else:
p = qparam.param.data.view(-1, self.group_size)
levels, scales = uniform_quantize(p, bits)
return levels, scales, bits
def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
levels, param_scale, bits = quantized
return uniform_unquantize(levels, param_scale, bits).view_as(qparam.param.data)
def detach(self):
super().detach()
for qparam in self._qparams:
delattr(qparam.module, qparam.name + self.suffix)
def __repr__(self):
return simple_repr(self)

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diffq/uniform.py
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# 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 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):
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 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()

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diffq/utils.py
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# 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 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)})"