from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import itertools
import warnings
import torch
import torch.nn as nn
import torch.nn.intrinsic as nni
import torch.nn.quantized as nnq
from .default_mappings import (DEFAULT_DYNAMIC_MODULE_MAPPING,
DEFAULT_MODULE_MAPPING,
DEFAULT_QAT_MODULE_MAPPING,
DEFAULT_QCONFIG_PROPAGATE_WHITE_LIST)
from .stubs import DeQuantStub, QuantWrapper
from .qconfig import default_dynamic_qconfig, float16_dynamic_qconfig
def _propagate_qconfig_helper(module, qconfig_dict, white_list=None,
qconfig_parent=None, prefix=''):
r"""This is a helper function for `propagate_qconfig_`
Args:
module: input module
qconfig_dict: dictionary that maps from name of submodule to quantization
configuration
white_list: list of quantizable modules
qconfig_parent: quantization config of parent module, we will fallback to
this config when there is no specified config for current
module
prefix: corresponding prefix of the current module, used as key in
qconfig_dict
Return:
None, module is modified inplace with qconfig attached
"""
# TODO: Add test
if white_list is None:
white_list = DEFAULT_QCONFIG_PROPAGATE_WHITE_LIST
module_qconfig = qconfig_dict.get(type(module), qconfig_parent)
module_qconfig = qconfig_dict.get(prefix, module_qconfig)
module_qconfig = getattr(module, 'qconfig', module_qconfig)
if type(module) in white_list:
module.qconfig = module_qconfig
for name, child in module.named_children():
module_prefix = prefix + '.' + name if prefix else name
_propagate_qconfig_helper(child, qconfig_dict, white_list,
module_qconfig, module_prefix)
# TODO(jerryzh): expose white_list
[docs]def propagate_qconfig_(module, qconfig_dict=None, white_list=None):
r"""Propagate qconfig through the module hierarchy and assign `qconfig`
attribute on each leaf module
Args:
module: input module
qconfig_dict: dictionary that maps from name or type of submodule to
quantization configuration, qconfig applies to all submodules of a
given module unless qconfig for the submodules are specified (when
the submodule already has qconfig attribute)
Return:
None, module is modified inplace with qconfig attached
"""
if qconfig_dict is None:
qconfig_dict = {}
_propagate_qconfig_helper(module, qconfig_dict, white_list)
def _observer_forward_hook(self, input, output):
r"""Forward hook that calls observer on the output
"""
return self.activation_post_process(output)
[docs]def add_observer_(module, non_leaf_module_list=None, device=None):
r"""Add observer for the leaf child of the module.
This function insert observer module to all leaf child module that
has a valid qconfig attribute.
Args:
module: input module with qconfig attributes for all the leaf modules that we want to quantize
device: parent device, if any
non_leaf_module_list: list of non-leaf modules we want to add observer
Return:
None, module is modified inplace with added observer modules and forward_hooks
"""
# respect device affinity when adding observers
if device is None:
devices = get_unique_devices_(module)
assert len(devices) <= 1, (
"add_observer_ only works with cpu or single-device CUDA modules, "
"but got devices {}".format(devices)
)
device = next(iter(devices)) if len(devices) > 0 else None
for child in module.children():
if type(child) == nnq.FloatFunctional or type(child) == nnq.QFunctional:
if hasattr(child, 'qconfig') and child.qconfig is not None:
activation = child.qconfig.activation()
if device is not None:
activation.to(device)
child.activation_post_process = activation
elif non_leaf_module_list is not None and type(child) in non_leaf_module_list:
if hasattr(child, 'qconfig') and child.qconfig is not None:
child.add_module('activation_post_process', child.qconfig.activation())
child.register_forward_hook(_observer_forward_hook)
else:
add_observer_(child, non_leaf_module_list, device)
# Insert observers only for leaf nodes, note that this observer is for
# the output of the module, for input QuantStub will observe them
if hasattr(module, 'qconfig') and module.qconfig is not None and \
len(module._modules) == 0 and not isinstance(module, torch.nn.Sequential):
# observer and hook will be gone after we swap the module
activation = module.qconfig.activation()
if device is not None:
activation.to(device)
module.add_module('activation_post_process', activation)
module.register_forward_hook(_observer_forward_hook)
def get_unique_devices_(module):
return {p.device for p in module.parameters()} | \
{p.device for p in module.buffers()}
[docs]def add_quant_dequant(module):
r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig
Note that this function will modify the children of module inplace and it
can return a new module which wraps the input module as well.
Args:
module: input module with qconfig attributes for all the leaf modules
that we want to quantize
Return:
Either the inplace modified module with submodules wrapped in
`QuantWrapper` based on qconfig or a new `QuantWrapper` module which
wraps the input module, the latter case only happens when the input
module is a leaf module and we want to quantize it.
"""
if len(module._modules) == 0 and hasattr(module, 'qconfig') and module.qconfig:
return QuantWrapper(module)
for name, child in module.named_children():
module._modules[name] = add_quant_dequant(child)
return module
[docs]def prepare(model, inplace=False, white_list=DEFAULT_QCONFIG_PROPAGATE_WHITE_LIST, observer_non_leaf_module_list=None):
r"""Prepares a copy of the model for quantization calibration or quantization-aware training.
Quantization configuration should be assigned preemptively
to individual submodules in `.qconfig` attribute.
The model will be attached with observer or fake quant modules, and qconfig
will be propagated.
Args:
model: input model to be modified in-place
inplace: carry out model transformations in-place, the original module is mutated
white_list: list of quantizable modules
observer_non_leaf_module_list: list of non-leaf modules we want to add observer
"""
if not inplace:
model = copy.deepcopy(model)
propagate_qconfig_(model, qconfig_dict=None, white_list=white_list)
# sanity check common API misusage
if not any(hasattr(m, 'qconfig') and m.qconfig for m in model.modules()):
warnings.warn("None of the submodule got qconfig applied. Make sure you "
"passed correct configuration through `qconfig_dict` or "
"by assigning the `.qconfig` attribute directly on submodules")
add_observer_(model, observer_non_leaf_module_list)
return model
def _remove_qconfig(module):
r"""Clean up the qconfig left in the module so that new qconfig can be
propagated.
Args:
module: module to be cleaned up
"""
for child in module.children():
_remove_qconfig(child)
if hasattr(module, "qconfig"):
del module.qconfig
[docs]def quantize(model, run_fn, run_args, mapping=None, inplace=False):
r"""Quantize the input float model with post training static quantization.
First it will prepare the model for calibration, then it calls
`run_fn` which will run the calibration step, after that we will
convert the model to a quantized model.
Args:
model: input float model
run_fn: a calibration function for calibrating the prepared model
run_args: positional arguments for `run_fn`
inplace: carry out model transformations in-place, the original module is mutated
mapping: correspondence between original module types and quantized counterparts
Return:
Quantized model.
"""
if mapping is None:
mapping = DEFAULT_MODULE_MAPPING
if not inplace:
model = copy.deepcopy(model)
model.eval()
prepare(model, inplace=True)
run_fn(model, run_args)
convert(model, mapping, inplace=True)
_remove_qconfig(model)
return model
[docs]def quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8,
mapping=None, inplace=False):
r"""Converts a float model to dynamic (i.e. weights-only) quantized model.
Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.
For simplest usage provide `dtype` argument that can be float16 or qint8. Weight-only quantization
by default is performed for layers with large weights size - i.e. Linear and RNN variants.
Fine grained control is possible with `qconfig` and `mapping` that act similarly to `quantize()`.
If `qconfig` is provided, the `dtype` argument is ignored.
Args:
module: input model
qconfig_spec: Either:
- A dictionary that maps from name or type of submodule to quantization
configuration, qconfig applies to all submodules of a given
module unless qconfig for the submodules are specified (when the
submodule already has qconfig attribute). Entries in the dictionary
need to be QConfigDynamic instances.
- A set of types and/or submodule names to apply dynamic quantization to,
in which case the `dtype` argument is used to specify the bit-width
inplace: carry out model transformations in-place, the original module is mutated
mapping: maps type of a submodule to a type of corresponding dynamically quantized version
with which the submodule needs to be replaced
"""
if qconfig_spec is None:
if dtype == torch.qint8:
qconfig_spec = {
nn.Linear : default_dynamic_qconfig,
nn.LSTM : default_dynamic_qconfig,
nn.GRU : default_dynamic_qconfig,
nn.LSTMCell : default_dynamic_qconfig,
nn.RNNCell : default_dynamic_qconfig,
nn.GRUCell : default_dynamic_qconfig,
}
elif dtype == torch.float16:
qconfig_spec = {
nn.Linear : float16_dynamic_qconfig,
nn.LSTM : float16_dynamic_qconfig,
nn.GRU : float16_dynamic_qconfig,
nn.LSTMCell : float16_dynamic_qconfig,
nn.RNNCell : float16_dynamic_qconfig,
nn.GRUCell : float16_dynamic_qconfig,
}
else:
raise ValueError(
"Don't know how to quantize with default settings for {}. Provide full qconfig please".format(dtype))
elif isinstance(qconfig_spec, set):
if dtype is torch.qint8:
default_qconfig = default_dynamic_qconfig
elif dtype is torch.float16:
default_qconfig = float16_dynamic_qconfig
else:
raise RuntimeError('Unknown dtype specified for quantize_dynamic: ', str(dtype))
qconfig_spec = dict(zip(qconfig_spec, itertools.repeat(default_qconfig)))
if mapping is None:
mapping = DEFAULT_DYNAMIC_MODULE_MAPPING
if not inplace:
model = copy.deepcopy(model)
model.eval()
propagate_qconfig_(model, qconfig_spec)
convert(model, mapping, inplace=True)
_remove_qconfig(model)
return model
[docs]def prepare_qat(model, mapping=None, inplace=False):
r"""
Prepares a copy of the model for quantization calibration or
quantization-aware training and converts it to quantized version.
Quantization configuration should be assigned preemptively
to individual submodules in `.qconfig` attribute.
Args:
model: input model to be modified in-place
mapping: dictionary that maps float modules to quantized modules to be
replaced.
inplace: carry out model transformations in-place, the original module
is mutated
"""
if mapping is None:
mapping = DEFAULT_QAT_MODULE_MAPPING
model = prepare(model, inplace=inplace)
convert(model, mapping, inplace=True)
return model
[docs]def quantize_qat(model, run_fn, run_args, inplace=False):
r"""Do quantization aware training and output a quantized model
Args:
model: input model
run_fn: a function for evaluating the prepared model, can be a
function that simply runs the prepared model or a training
loop
run_args: positional arguments for `run_fn`
Return:
Quantized model.
"""
if not inplace:
model = copy.deepcopy(model)
model.train()
prepare_qat(model, inplace=True)
run_fn(model, run_args)
convert(model, inplace=True)
return model
[docs]def convert(module, mapping=None, inplace=False):
r"""Converts the float module with observers (where we can get quantization
parameters) to a quantized module.
Args:
module: calibrated module with observers
mapping: a dictionary that maps from float module type to quantized
module type, can be overwritten to allow swapping user defined
Modules
inplace: carry out model transformations in-place, the original module
is mutated
"""
if mapping is None:
mapping = DEFAULT_MODULE_MAPPING
if not inplace:
module = copy.deepcopy(module)
reassign = {}
# TODO(jerryzh): remove after deciding on the impl of intrinsic modules
# This is required because intrinsic modules right now are implemented as
# nn.Sequential and we don't want to swap their constituents
SWAPPABLE_MODULES = (nni.ConvBn2d,
nni.ConvBnReLU2d,
nni.LinearReLU,
nni.BNReLU2d,
nni.BNReLU3d,
nni.ConvBn1d,
nni.ConvReLU1d,
nni.ConvBnReLU1d,
nni.ConvReLU2d,
nni.ConvReLU3d)
for name, mod in module.named_children():
if type(mod) not in SWAPPABLE_MODULES:
convert(mod, mapping, inplace=True)
reassign[name] = swap_module(mod, mapping)
for key, value in reassign.items():
module._modules[key] = value
return module
[docs]def swap_module(mod, mapping):
r"""Swaps the module if it has a quantized counterpart and it has an
`observer` attached.
Args:
mod: input module
mapping: a dictionary that maps from nn module to nnq module
Return:
The corresponding quantized module of `mod`
"""
new_mod = mod
# Always replace dequantstub with dequantize
if hasattr(mod, 'qconfig') and mod.qconfig is not None or type(mod) == DeQuantStub:
if type(mod) in mapping:
# respect device affinity when swapping modules
devices = get_unique_devices_(mod)
assert len(devices) <= 1, (
"swap_module only works with cpu or single-device CUDA modules, "
"but got devices {}".format(devices)
)
device = next(iter(devices)) if len(devices) > 0 else None
new_mod = mapping[type(mod)].from_float(mod)
if device:
new_mod.to(device)
return new_mod
[docs]def get_observer_dict(mod, target_dict, prefix=""):
r"""Traverse the modules and save all observers into dict.
This is mainly used for quantization accuracy debug
Args:
mod: the top module we want to save all observers
prefix: the prefix for the current module
target_dict: the dictionary used to save all the observers
"""
def get_prefix(prefix):
return prefix if prefix == "" else prefix + '.'
if hasattr(mod, 'activation_post_process'):
target_dict[get_prefix(prefix) + 'activation_post_process'] = mod.activation_post_process
for name, child in mod.named_children():
module_prefix = get_prefix(prefix) + name if prefix else name
get_observer_dict(child, target_dict, module_prefix)
